Relevant bibliographies by topics / A Priori and a Posteriori Knowledge

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Academic literature on the topic 'A Priori and a Posteriori Knowledge'

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Published: 4 June 2025
Last updated: 23 June 2025

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Journal articles on the topic "A Priori and a Posteriori Knowledge"

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Johnson, John A. "A posteriori & a priori knowledge in Taekwondo." Revista de Artes Marciales Asiáticas 11, no. 2s (2016): 98. http://dx.doi.org/10.18002/rama.v11i2s.4191.

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McLeod, Stephen K. "Knowledge of Necessity: Logical Positivism and Kripkean Essentialism." Philosophy 83, no. 2 (2008): 179–91. http://dx.doi.org/10.1017/s0031819108000454.

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AbstractBy the lights of a central logical positivist thesis in modal epistemology, for every necessary truth that we know, we know it a priori and for every contingent truth that we know, we know it a posteriori. Kripke attacks on both flanks, arguing that we know necessary a posteriori truths and that we probably know contingent a priori truths. In a reflection of Kripke's confidence in his own arguments, the first of these Kripkean claims is far more widely accepted than the second. Contrary to received opinion, the paper argues, the considerations Kripke adduces concerning truths purported to be necessary a posteriori do not disprove the logical positivist thesis that necessary truth and a priori truth are co-extensive.
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Lane, Ashley. "ARE MORAL FUNCTIONALISM’S MORAL A PRIORI COMMITMENTS REALLY A PRIORI?" Ethics, Politics & Society 1 (May 14, 2018): 15. http://dx.doi.org/10.21814/eps.1.1.53.

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Moral functionalism, a metaethical theory developed by Frank Jackson and Philip Pettit, claims that we can attain moral knowledge by ascertaining the commonplaces about morality that are typically accepted by actual agents. It has important a priori commitments; whilst we may discover a posteriori that a particular descriptive property is identical to a particular moral property, it is a priori that the thing that is identical to the moral property, whatever that thing actually is, plays a particular role. Jackson holds a particular metaphysical position, and moral functionalism is a development of that position as it applies to ethics. In this paper I adapt an objection made by D.H. Mellor against Jackson’s metaphysics to show that moral functionalism’s a priori commitments are actually a posteriori. We can only discover if moral functionalism’s purportedly a priori claims are true through a posteriori investigation.
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Casullo, Albert. "Necessity, Certainty, and the A Priori." Canadian Journal of Philosophy 18, no. 1 (1988): 43–66. http://dx.doi.org/10.1080/00455091.1988.10717165.

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Empiricist theories of knowledge are attractive for they offer the prospect of a unitary theory of knowledge based on relatively well understood physiological and cognitive processes. Mathematical knowledge, however, has been a traditional stumbling block for such theories. There are three primary features of mathematical knowledge which have led epistemologists to the conclusion that it cannot be accommodated within an empiricist framework: 1) mathematical propositions appear to be immune from empirical disconfirmation; 2) mathematical propositions appear to be known with certainty; and 3) mathematical propositions are necessary. Epistemologists who believe that some nonmathematical propositions, such as logical or ethical propositions, cannot be known a posteriori also typically appeal to the three factors cited above in defending their position. The primary purpose of this paper is to examine whether any of these alleged features of mathematical propositions establishes that knowledge of such propositions cannot be a posteriori.
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Tropman, Elizabeth. "Self-Evidence and A Priori Moral Knowledge." Disputatio 4, no. 33 (2012): 459–67. http://dx.doi.org/10.2478/disp-2012-0011.

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Abstract According to rationalists about moral knowledge, some moral truths are knowable a priori. Rationalists often defend their position by claiming that some moral propositions are self-evidently true. Copp 2007 has recently challenged this rationalist strategy. Copp argues that even if some moral propositions are self-evident, this is not enough to secure rationalism about moral knowledge, since it turns out that such self-evident propositions are only knowable a posteriori. This paper considers the merits of Copp’s challenge. After clarifying the rationalists’ appeal to self-evidence, I show why this rationalist strategy survives Copp’s challenges to it.
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Williamson, Timothy. "Unexceptional Moral Knowledge." Journal of Chinese Philosophy 49, no. 4 (2022): 405–15. http://dx.doi.org/10.1163/15406253-12340082.

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Abstract The article defends moral realism against epistemological objections by arguing that if there are moral truths, some of them are known. The claim that moral properties are unknowable because causally inert is shown to be ineffective: none of the main current theories of knowledge requires a causal connection, and anyway moral properties have not been shown to be causally inert. It is explained why a posteriori moral knowledge need not derive from combining a priori moral knowledge with a posteriori non-moral knowledge. The possibility of moral knowledge by perception and by testimony is briefly defended. The role of recognitional capacities for instances of moral properties is emphasized.
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Smith, Renée. "MOORE AND DESCARTES MEET IN A BAR." Think 11, no. 31 (2012): 21–26. http://dx.doi.org/10.1017/s147717561200005x.

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Philosophers typically distinguish between a priori and a posteriori beliefs, knowledge, justification, and propositions. A belief is a priori if it is derived from reason, and it is a posteriori if it is derived from sense experience. Similarly, we would say that we know a priori that ‘a closed, n-sided figure has n interior angles’ because our knowledge is derived from reason in that we understand the concept of a closed, n-sided figure and thus know the statement is true. On the other hand, we know a posteriori that ‘Americans drive on the right’ because in justifying this belief, we appeal to sense experience; perhaps we have seen for ourselves that Americans drive on the right or we've read about it in a book or seen it in a movie.
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Müller-Merbach, Heiner. "Kant's two paths of knowledge creation: a priori vs a posteriori." Knowledge Management Research & Practice 5, no. 1 (2007): 64–65. http://dx.doi.org/10.1057/palgrave.kmrp.8500123.

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Beebe, James R. "A Priori Skepticism and the KK Thesis." International Journal for the Study of Skepticism 5, no. 4 (2015): 315–26. http://dx.doi.org/10.1163/22105700-04010005.

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In a previous article, I argued against the widespread reluctance of philosophers to treat skeptical challenges to our a priori knowledge of necessary truths with the same seriousness as skeptical challenges to our a posteriori knowledge of contingent truths. Hamid Vahid has recently offered several reasons for thinking the unequal treatment of these two kinds of skepticism is justified, one of which is a priori skepticism’s seeming dependence upon the widely scorned kk thesis. In the present article, I defend a priori skepticism against Vahid’s criticisms.
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Cordua, Knud Skou, Thomas Mejer Hansen, and Klaus Mosegaard. "Monte Carlo full-waveform inversion of crosshole GPR data using multiple-point geostatistical a priori information." GEOPHYSICS 77, no. 2 (2012): H19—H31. http://dx.doi.org/10.1190/geo2011-0170.1.

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We present a general Monte Carlo full-waveform inversion strategy that integrates a priori information described by geostatistical algorithms with Bayesian inverse problem theory. The extended Metropolis algorithm can be used to sample the a posteriori probability density of highly nonlinear inverse problems, such as full-waveform inversion. Sequential Gibbs sampling is a method that allows efficient sampling of a priori probability densities described by geostatistical algorithms based on either two-point (e.g., Gaussian) or multiple-point statistics. We outline the theoretical framework for a full-waveform inversion strategy that integrates the extended Metropolis algorithm with sequential Gibbs sampling such that arbitrary complex geostatistically defined a priori information can be included. At the same time we show how temporally and/or spatiallycorrelated data uncertainties can be taken into account during the inversion. The suggested inversion strategy is tested on synthetic tomographic crosshole ground-penetrating radar full-waveform data using multiple-point-based a priori information. This is, to our knowledge, the first example of obtaining a posteriori realizations of a full-waveform inverse problem. Benefits of the proposed methodology compared with deterministic inversion approaches include: (1) The a posteriori model variability reflects the states of information provided by the data uncertainties and a priori information, which provides a means of obtaining resolution analysis. (2) Based on a posteriori realizations, complicated statistical questions can be answered, such as the probability of connectivity across a layer. (3) Complex a priori information can be included through geostatistical algorithms. These benefits, however, require more computing resources than traditional methods do. Moreover, an adequate knowledge of data uncertainties and a priori information is required to obtain meaningful uncertainty estimates. The latter may be a key challenge when considering field experiments, which will not be addressed here.
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Dissertations / Theses on the topic "A Priori and a Posteriori Knowledge"

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Zhou, Hao. "La chute du "triangle d'or" : apriorité, analyticité, nécessité : de l'équivalence à l'indépendance." Thesis, Paris 1, 2020. http://www.theses.fr/2020PA01H204.

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Les trois concepts d’apriorité, d’analyticité et de nécessité, qui ont longtemps été considérés comme équivalents, constituent ce que l’on peut appeler le « triangle d’or » ou « triangle d’équivalence ». Or, la conception kantienne du synthétique a priori et les conceptions kripkéennes du contingent a priori et du nécessaire a posteriori représentent des critiques décisives contre ce triangle d’équivalence. Héritant, de manière critique, des idées révolutionnaires de Kant et de Kripke, un nouveau schéma épistémologique intitulé « sujet-connaissance-monde » est ici systématiquement construit. Ce schéma rend totalement caduc le triangle d’or. Les concepts d’apriorité, d’analyticité et de nécessité deviennent indépendants les uns des autres. On aboutit ainsi à un nouvel espace des catégories de la connaissance, issu du libre entre croisement des trois distinctions a priori-a posteriori, analytique-synthétique et nécessaire-contingent. Ces catégories de la connaissance, dont certaines sont nouvelles, s’appliquent aux sciences exclusivement et exhaustivement<br>The three concepts of apriority, analyticity and necessity, which have long been considered equivalent, constitute whatcould be called the “golden triangle” or “triangle of equivalence”. Yet, the Kantian conception of the synthetic a priori and the Kripkean conceptions of the contingent a priori and the necessary a posteriori represent decisive criticismsagainst this triangle of equivalence. Inheriting critically these revolutionary thoughts from Kant and Kripke, a newepistemological schema entitled “subject-knowledge-world” is here systematically constructed. This schema renders thegolden triangle totally obsolete. The concepts of apriority, analyticity and necessity become independent of each other.This leads to a new space of knowledge categories, resulting from the free intersecting of the three distinctions a priori-aposteriori, analytic-synthetic and necessary-contingent. These knowledge categories, some of which are new, apply to science exclusively and exhaustively
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CHAN, Hiu Man. "Is there a distinction between a priori and a posteriori." Digital Commons @ Lingnan University, 2014. https://commons.ln.edu.hk/philo_etd/10.

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This thesis studies whether there is a tenable distinction between a priori justification and a posteriori justification. My research considers three possible conceptions of a priori: (1) Justification Independent of Experience, (2) Mere Meaning Based Justification and (3) Justification by Rational Insight, and examines whether they can provide a sound and significant distinction between a priori and a posteriori. This thesis contains five chapters. Chapter 1 introduces the background knowledge of the a priori/a posteriori distinction. Chapter 2 analyzes the traditional conception of a priori, i.e. justification independent of experience, and considers whether the distinction based on it is tenable. Five approaches for defining “experience” are examined, but none of them succeed in providing a distinction between a priori and a posteriori. Chapter 3 focuses on the empiricist conception of the a priori, i.e. a priori as mere meaning based justification, and argues that the distinction based on it has a problem of classification. Chapter 4 concerns the rationalist conception of the a priori, i.e. a priori as justification by rational insight, and argues that neither the idea of justification by rational insight itself nor the distinctive features of rational insight could provide a distinction between a priori and a posteriori. Given that none of the current major accounts seem to work, we should not be optimistic about the potential for success in accounting for the distinction between a priori and a posteriori. In the last chapter, I will conclude the thesis and point out the implication of abandoning the a priori/a posteriori distinction: a need to reform our understanding of the nature of different sources of justification and knowledge.
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Bourgeade, Tom. "Interprétabilité a priori et explicabilité a posteriori dans le traitement automatique des langues." Thesis, Toulouse 3, 2022. http://www.theses.fr/2022TOU30063.

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Avec l'avènement des architectures Transformer en Traitement Automatique des Langues il y a quelques années, nous avons observé des progrès sans précédents dans diverses tâches de classification ou de génération de textes. Cependant, l'explosion du nombre de paramètres et de la complexité de ces modèles "boîte noire" de l'état de l'art, rendent de plus en plus évident le besoin désormais urgent de transparence dans les approches d'apprentissage automatique. La capacité d'expliquer, d'interpréter et de comprendre les décisions algorithmiques deviendra primordiale à mesure que les modèles informatiques deviennent de plus en plus présents dans notre vie quotidienne. En utilisant les méthodes de l'IA eXplicable (XAI), nous pouvons par exemple diagnostiquer les biais dans des ensembles de données, des corrélations erronées qui peuvent au final entacher le processus d'apprentissage des modèles, les conduisant à apprendre des raccourcis indésirables, ce qui pourrait conduire à des décisions algorithmiques injustes, incompréhensibles, voire risquées. Ces modes d'échec de l'IA peuvent finalement éroder la confiance que les humains auraient pu placer dans des applications bénéfiques. Dans ce travail, nous explorons plus spécifiquement deux aspects majeurs de l'XAI, dans le contexte des tâches et des modèles de Traitement Automatique des Langues : dans la première partie, nous abordons le sujet de l'interprétabilité intrinsèque, qui englobe toutes les méthodes qui sont naturellement faciles à expliquer. En particulier, nous nous concentrons sur les représentations de plongement de mots, qui sont une composante essentielle de pratiquement toutes les architectures de TAL, permettant à ces modèles mathématiques de manipuler le langage humain d'une manière plus riche sur le plan sémantique. Malheureusement, la plupart des modèles qui génèrent ces représentations les produisent d'une manière qui n'est pas interprétable par les humains. Pour résoudre ce problème, nous expérimentons la construction et l'utilisation de modèles de plongement de mots interprétables, qui tentent de corriger ce problème, en utilisant des contraintes qui imposent l'interprétabilité de ces représentations. Nous utilisons ensuite ces modèles, dans une configuration nouvelle, simple mais efficace, pour tenter de détecter des corrélations lexicales, erronées ou non, dans certains ensembles de données populaires en TAL. Dans la deuxième partie, nous explorons les méthodes d'explicabilité post-hoc, qui peuvent cibler des modèles déjà entraînés, et tenter d'extraire diverses formes d'explications de leurs décisions. Ces méthodes peuvent aller du diagnostic des parties d'une entrée qui étaient les plus pertinentes pour une décision particulière, à la génération d'exemples adversariaux, qui sont soigneusement conçus pour aider à révéler les faiblesses d'un modèle. Nous explorons un nouveau type d'approche, en partie permis par les architectures Transformer récentes, très performantes mais opaques : au lieu d'utiliser une méthode distincte pour produire des explications des décisions d'un modèle, nous concevons et mettons au point une configuration qui apprend de manière jointe à exécuter sa tâche, tout en produisant des explications en langage naturel en forme libre de ses propres résultats. Nous évaluons notre approche sur un ensemble de données de grande taille annoté avec des explications humaines, et nous jugeons qualitativement certaines des explications générées par notre approche<br>With the advent of Transformer architectures in Natural Language Processing a few years ago, we have observed unprecedented progress in various text classification or generation tasks. However, the explosion in the number of parameters, and the complexity of these state-of-the-art blackbox models, is making ever more apparent the now urgent need for transparency in machine learning approaches. The ability to explain, interpret, and understand algorithmic decisions will become paramount as computer models start becoming more and more present in our everyday lives. Using eXplainable AI (XAI) methods, we can for example diagnose dataset biases, spurious correlations which can ultimately taint the training process of models, leading them to learn undesirable shortcuts, which could lead to unfair, incomprehensible, or even risky algorithmic decisions. These failure modes of AI, may ultimately erode the trust humans may have otherwise placed in beneficial applications. In this work, we more specifically explore two major aspects of XAI, in the context of Natural Language Processing tasks and models: in the first part, we approach the subject of intrinsic interpretability, which encompasses all methods which are inherently easy to produce explanations for. In particular, we focus on word embedding representations, which are an essential component of practically all NLP architectures, allowing these mathematical models to process human language in a more semantically-rich way. Unfortunately, many of the models which generate these representations, produce them in a way which is not interpretable by humans. To address this problem, we experiment with the construction and usage of Interpretable Word Embedding models, which attempt to correct this issue, by using constraints which enforce interpretability on these representations. We then make use of these, in a simple but effective novel setup, to attempt to detect lexical correlations, spurious or otherwise, in some popular NLP datasets. In the second part, we explore post-hoc explainability methods, which can target already trained models, and attempt to extract various forms of explanations of their decisions. These can range from diagnosing which parts of an input were the most relevant to a particular decision, to generating adversarial examples, which are carefully crafted to help reveal weaknesses in a model. We explore a novel type of approach, in parts allowed by the highly-performant but opaque recent Transformer architectures: instead of using a separate method to produce explanations of a model's decisions, we design and fine-tune an architecture which jointly learns to both perform its task, while also producing free-form Natural Language Explanations of its own outputs. We evaluate our approach on a large-scale dataset annotated with human explanations, and qualitatively judge some of our approach's machine-generated explanations
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Kroedel, Thomas. "A priori knowledge of modal truths." Thesis, University of Oxford, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.440706.

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Midelfart, Herman. "Knowledge discovery from cDNA microarrays and a priori knowledge." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2003. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-912.

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Microarray technology has recently attracted a lot of attention. This technology can measure the behavior (i.e., RNA abundance) of thousands of genes simultaneously, while previous methods have only allowed measurements of single genes. By enabling studies on a genome-wide scale, microarray technology is currently revolutionizing biological research and creating a wide range of research opportunities. However, the technology generates a vast amount of data that cannot be handled manually. Computational analysis is thus a prerequisite for the success of this technology, and research and development of computational tools for microarray analysis are of great importance. This thesis develops supervised learning methods based on Rough Set Theory (RST) for analyzing microarray data together with prior knowledge. Two kinds of microarray studies are considered. The first is cancer studies where supervised learning may be used for predicting tumor subtypes and clinical parameters. We introduce a general RST approach for classification of tumor samples analyzed by microarrays. This includes a feature selection method for selecting genes that discriminate significantly between a set of classes. RST classifiers are then learned from the selected genes. The approach is applied to a data set of gastric tumors. Classifiers for six clinical parameters are developed and demonstrate that these parameters can be predicted from the expression profile of gastric tumors. Moreover, the performance of the feature selection method as well as several learning and discretization methods implemented in ROSETTA are examined and compared to the performance of linear and quadratic discrimination analysis. The classifiers are also biologically validated. One of the best classifiers is selected for each clinical parameter, and the connection between the genes used in these classifiers and the parameters are compared to the established knowledge in the biomedical literature. Many of these genes have no previously known connection to gastric cancer and provide interesting targets for further biological research. The second kind of study is prediction of gene function from expression profiles measured with microarrays. A serious problem in this case is that functional classes, which are assigned to genes, are typically organized in an ontology where the classes may be related to each other. One example is the Gene Ontology where the classes form a Directed Acyclic Graph (DAG). Standard learning methods such as RST assume, however, that the classes are unrelated, and cannot deal with this problem directly. This thesis gives a solution by introducing an extended RST framework and two novel algorithms for learning in a DAG. The DAG also constitutes a problem when a classifier is to be evaluated since standard performance measures such as accuracy or AUC do not recognize the structure of the DAG. Therefore, several new performance measures are introduced. The algorithms are first tested on a data set that was created from human fibroblast cells by the means of microarrays. They are then applied on artificial data in order to obtain a better understanding of their behavior, and their weaknesses and strengths are identified.
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El, Alaoui Lakhnati Linda. "Analyse d'erreur a priori et a posteriori pour des méthodes d'éléments finis mixtes non-conformes." Phd thesis, Ecole des Ponts ParisTech, 2005. http://pastel.archives-ouvertes.fr/pastel-00001267.

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Dans cette thèse nous nous intéressons à l'analyse d'erreur a priori et a posteriori de méthodes d'éléments finis mixtes et non-conformes. Nous considérons en particulier les équations de Darcy à perméabilité fortement variable et les équations de convection-diffusion-réaction en régime de convection dominante. Nous discrétisons les équations de Darcy par une méthode d'éléments finis mixtes non-conformes de type Petrov-Galerkin appelée schéma boîte. Les techniques d'estimations d'erreur a posteriori par résidu et hiérarchique conduisent à des estimateurs d'erreur a posteriori fiables et optimaux indépendamment des fluctuations de la perméabilité. Les résultats théoriques sont validés numériquement sur différents cas tests présentant de forts contrastes de perméabilité. Enfin, nous montrons comment les indicateurs d'erreur obtenus permettent de générer des maillages adaptatifs. Nous discrétisons les équations de convection-diffusion-réaction par des éléments finis nonconformes. Deux méthodes de stabilisation sont étudiées: la stabilisation par viscosité de sous-maille, conduisant à un schéma boîte et la méthode de pénalisation sur les faces. Nous montrons que les deux schémas ainsi obtenus ont les mêmes propriétés de convergence que les approximations par éléments finis conformes. Grâce aux techniques d'estimations d'erreur par résidu nous obtenons des estimateurs d'erreur a posteriori fiables et optimaux. Certains des indicateurs d'erreur sont robustes au sens de Verfürth, c'est à dire que le rapport des constantes intervenant dans les inégalités de fiabilité et d'optimalité explose en au plus l'inverse du nombre de Péclet. Les résultats théoriques sont validés numériquement et les indicateurs d'erreur a posteriori obtenus permettent de générer des maillages adaptatifs sur des problèmes présentant des couches intérieures.
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Langlois, Xavier. "Adaptation a priori et a posteriori de maillage autour d'une interface dans des problèmes thermiques." Nancy 1, 1993. http://www.theses.fr/1993NAN10178.

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L'objet de ce travail est l'analyse mathématique et numérique d'un critère de maillage au voisinage de l'interface entre deux milieux pour un problème de transfert thermique. Nous caractérisons son domaine de validité pour diverses méthodes d'approximation numérique et nous établissons le lien avec les techniques usuelles de maillage adaptatif. Une première analyse montre que le critère est efficace sur certains schémas monodimensionnels aux différences finies à trois points en espace et qu'il se traduit par une amélioration de l'erreur de consistance au point interface. Dans le cadre de la méthode aux éléments finis de degré un, nous montrons que le critère coïncide avec celui classique d'equirepartition de la norme l#2 de l'erreur d'interpolation. Par contre, pour d'autres normes, en particulier la norme énergie, l'analyse conduit à des critères différents. Ensuite, nous situons le critère par rapport aux estimations a posteriori faites en maillage adaptatif pour des estimateurs équivalents à la norme énergie de l'erreur. Des simulations numériques illustrent tous ces aspects en dimension un et deux
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El, Alaoui Lakhnati Linda. "Analyse d'esrreur a priori et a posteriori pour des méthodes d'éléments finis mixtes non-conformes." Marne-la-vallée, ENPC, 2005. https://pastel.archives-ouvertes.fr/pastel-00001267.

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Lane, Ashley Alexander. "A critique of a priori moral knowledge." Thesis, Birkbeck (University of London), 2018. http://bbktheses.da.ulcc.ac.uk/368/.

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Many ethicists believe that if it is possible to know a true moral proposition, it is always possible to ascertain a priori the normative content of that proposition. I argue that this is wrong; the only way to ascertain the normative content of some moral propositions requires the use of a posteriori information. I examine what I call determinate core moral propositions. I assume that some of these propositions are true and that actual agents are able to know them. Ethicists whom I call coreapriorists believe that it is always possible to ascertain a priori the normative content of such propositions. Core-aposteriorists believe that this is false, and that sometimes a posteriori information must be used to ascertain that normative content. I develop what I call the a posteriori strategy to show that core-apriorists are likely to be wrong, and so core-aposteriorists are correct. The strategy examines the details of particular core-apriorist theories and then shows that the theories have one of two problems: either some of the knowable determinate core moral propositions in the theories are not knowable a priori, or some of the propositions are not determinate, so they cannot perform the epistemological work required of them. Therefore, some knowable determinate core moral propositions are only knowable with the aid of a posteriori information. I apply the strategy to four different core-apriorist theories. The first is Henry Sidgwick's theory of self-evident moral axioms, as recently developed by Katarzyna de Lazari-Radek and Peter Singer. The second is Matthew Kramer's moral realism. I then examine Michael Smith's moral realism, and Frank Jackson and Philip Pettit's moral functionalism. The a posteriori strategy shows that there are serious difficulties with all four theories. I conclude that it provides good evidence that the core-apriorist is mistaken, and that the core-aposteriorist is right.
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Al-Ghufli, Saeed M. A. O. "A reconsideration of constitutional review in the United Arab Emirates : 'a posteriori' or 'a priori' review?" Thesis, Aberystwyth University, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.252170.

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Books on the topic "A Priori and a Posteriori Knowledge"

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Kuttler, J. R. Estimating eigenvalues with a posteriori / a priori inequalities. Pitman Advanced, 1985.

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1937-, Sigillito V. G., ed. Estimating eigenvalues with a posteriori/a priori inequalities. Pitman Advanced Pub. Program, 1985.

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Albert, Casullo, ed. A priori knowledge. Ashgate, 1999.

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1957-, Moser Paul K., ed. A priori knowledge. Oxford University Press, 1987.

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Neveu, Sébastien. L'a priori, l'a posteriori, le pur, le non pur chez Christian Wolff et ses maîtres. Georg Olms Verlag, 2017.

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L, Camp Joseph, ed. The epistemology of a priori knowledge. Oxford University Press, 2005.

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Grozdanoff, Boris D. A priori revisability in science. Cambridge Scholars Publishing, 2014.

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Grondin, Jean. Kant et le problème de la philosophie: L'a priori. J. Vrin, 1989.

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Vavrik, Ursula. A priori and a posteriori travel market segmentation: Tailoring automatic interaction detection and cluster analysis for tourism marketing. Centre des Hautes Etudes Touristiques, 1990.

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Fred-Rivera, Ivette. A Historical and Systematic Perspective on A Priori Knowledge and Justification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06874-4.

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Book chapters on the topic "A Priori and a Posteriori Knowledge"

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Sun, Zengguo, and Xuejun Peng. "Maximum a Posteriori Despeckling Algorithm of Synthetic Aperture Radar Images with Exponential Prior Distribution." In Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-70665-4_47.

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Spurway, Neil. "A Priori/A Posteriori." In Encyclopedia of Sciences and Religions. Springer Netherlands, 2013. http://dx.doi.org/10.1007/978-1-4020-8265-8_200088.

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Tahko, Tuomas E. "A Priori or a Posteriori?" In The Routledge Handbook of Metametaphysics. Routledge, 2020. http://dx.doi.org/10.4324/9781315112596-27.

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Russell, Bruce. "A Priori vs. A Posteriori Justification." In Seemings. Routledge, 2023. http://dx.doi.org/10.4324/9781003299349-20.

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Lapointe, Sandra. "A priori Knowledge." In Bolzano's Theoretical Philosophy. Palgrave Macmillan UK, 2011. http://dx.doi.org/10.1057/9780230308640_9.

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Russell, David W. "A Priori Knowledge." In The BOXES Methodology Second Edition. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86069-1_14.

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Ostrowski, Alexander. "Über Fehlerabschaetzungen a priori und a posteriori." In Collected Mathematical Papers. Birkhäuser Basel, 1985. http://dx.doi.org/10.1007/978-3-0348-9336-7_40.

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Braddon-Mitchell, David. "A Priori Analysis and the Methodological A Posteriori." In The Palgrave Handbook of Philosophical Methods. Palgrave Macmillan UK, 2015. http://dx.doi.org/10.1057/9781137344557_2.

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Deng, Zhi-Hong. "An Improvement of Posteriori." In Fuzzy Systems and Knowledge Discovery. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_63.

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Hens, Niel, Ziv Shkedy, Marc Aerts, Christel Faes, Pierre Van Damme, and Philippe Beutels. "A Priori and A Posteriori Models for Infectious Diseases." In Statistics for Biology and Health. Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-4072-7_2.

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Conference papers on the topic "A Priori and a Posteriori Knowledge"

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Peng, Peixi, Wanshu Fan, Wenfei Liu, Xing Yang, and Dongsheng Zhou. "Prior-Posterior Knowledge Prompting-and-Reasoning for Surgical Visual Question Localized-Answering." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650493.

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Lin, Chenglin, Huimin Lu, Songzhe Ma, Zexing Zhang, Guolian Qi, and Yilong Wang. "PIKGMA: PrIori Knowledge-Guided Multimodal Alignment And Domain Adaptation For Emotion Recognition." In 2025 IEEE 17th International Conference on Computer Research and Development (ICCRD). IEEE, 2025. https://doi.org/10.1109/iccrd64588.2025.10963268.

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Potyka, Nico, and Richard Booth. "Balancing Open-Mindedness and Conservativeness in Quantitative Bipolar Argumentation (and How to Prove Semantical from Functional Properties)." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/56.

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Quantitative bipolar argumentation frameworks (QBAFs) have various applications in areas like product recommendation, review aggregation and explaining machine learning models. QBAF semantics assign a strength to every argument that is based on an a priori belief and the strength of its attackers and supporters. Intuitively, a QBAF semantics is open-minded when it is unbiased in the sense that a priori beliefs can be given up eventually when sufficient arguments to the contrary are presented. While this behaviour is desirable in many applications, existing open-minded semantics also have the property that even very weak arguments will eventually eliminate the a priori beliefs. In this paper, we will study notions of conservativeness that demand that the deviation from the a priori beliefs is bounded by the strength of pro and contra arguments. We will discuss compatibility and conflicts with existing properties and present two new semantics with interesting semantical guarantees. To do so, we will build up on the framework of modular semantics and prove some general relationships between functional and semantical properties that are useful to simplify the study of new modular semantics.
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Li, Yang, Aimin Zhou, and Guixu Zhang. "A probability model based evolutionary algorithm with priori and posteriori knowledge for multiobjective knapsack problems." In 2014 11th World Congress on Intelligent Control and Automation (WCICA). IEEE, 2014. http://dx.doi.org/10.1109/wcica.2014.7052912.

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Taylor, Graham W., Michael L. Seltzer, and Alex Acero. "Maximum a posteriori ICA: Applying prior knowledge to the separation of acoustic sources." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517986.

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Subbarayalu, Sethuramalingam, and Lonny L. Thompson. "HP-Adaptive Time-Discontinuous Galerkin Finite Element Methods for Time-Dependent Waves." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60403.

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hp-Adaptive time-discontinuous Galerkin methods are developed for second-order hyperbolic systems. Explicit a priori error estimates in terms of time-step size, approximation order, and solution regularity are derived. Knowledge of these a priori convergence rates in combination with a posteriori error estimates computed from the jump in time-discontinuous solutions are used to automatically select time-step size h and approximation order p to achieve a specified error tolerance with a minimal number of total degrees-of-freedom. We show that the temporal jump error is a good indicator of the local error, and the summation of jump error for the total interval is good indicator for the global and accumulation errors. In addition, the accumulation error at the end of a time-step can be estimated well by the summation of the local jump error at the beginning of a time-step provided the approximation order is greater or equal to the solution regularity. Superconvergence of the end points of a time-step for high-order polynomials are also demonstrated.
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Couto, Luis D., Dong Zhang, Antti Aitio, Scott Moura, and David Howey. "Estimation of Parameter Probability Distributions for Lithium-Ion Battery String Models Using Bayesian Methods." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3218.

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Abstract This paper addresses the parameter estimation problem for lithium-ion battery pack models comprising cells in series. This valuable information can be exploited in fault diagnostics to estimate the number of cells that are exhibiting abnormal behaviour, e.g. large resistances or small capacities. In particular, we use a Bayesian approach to estimate the parameters of a two-cell arrangement modelled using equivalent circuits. Although our modeling framework has been extensively reported in the literature, its structural identifiability properties have not been reported yet to the best of the authors’ knowledge. Moreover, most contributions in the literature tackle the estimation problem through point-wise estimates assuming Gaussian noise using e.g. least-squares methods (maximum likelihood estimation) or Kalman filters (maximum a posteriori estimation). In contrast, we apply methods that are suitable for nonlinear and non-Gaussian estimation problems and estimate the full posterior probability distribution of the parameters. We study how the model structure, available measurements and prior knowledge of the model parameters impact the underlying posterior probability distribution that is recovered for the parameters. For two cells in series, a bimodal distribution is obtained whose modes are centered around the real values of the parameters for each cell. Therefore, bounds on the model parameters for a battery pack can be derived.
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Biswas, Arpan, Claudio Fuentes, and Christopher Hoyle. "An Approach to Bayesian Optimization in Optimizing Weighted Tchebycheff Multi-Objective Black-Box Functions." In ASME 2020 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/imece2020-23414.

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Abstract Bayesian optimization (BO) is a low-cost global optimization tool for expensive black-box objective functions, where we learn from prior evaluated designs, update a posterior surrogate Gaussian process model, and select new designs for future evaluation using an acquisition function. This research focuses upon developing a BO model with multiple black-box objective functions. In the standard multi-objective optimization problem, the weighted Tchebycheff method is efficiently used to find both convex and non-convex Pareto frontier. This approach requires knowledge of utopia values before we start optimization. However, in the BO framework, since the functions are expensive to evaluate, it is very expensive to obtain the utopia values as a priori knowledge. Therefore, in this paper, we develop a Multi-Objective Bayesian Optimization (MO-BO) framework where we calibrate with Multiple Linear Regression (MLR) models to estimate the utopia value for each objective as a function of design input variables; the models are updated iteratively with sampled training data from the proposed multi-objective BO. The iteratively estimated mean utopia values are used to formulate the weighted Tchebycheff multi-objective acquisition function. The proposed approach is implemented in optimizing a thin tube design under constant loading of temperature and pressure, with multiple objectives such as minimizing the risk of creep-fatigue failure and design cost along with risk-based and manufacturing constraints. Finally, the model accuracy with and without MLR-based calibration is compared to the true Pareto solutions. The results show potential broader impacts, future research directions for further improving the proposed MO-BO model, and potential extensions to the application of large-scale design problems.
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Hemchandra, Santosh, Anindya Datta, and Matthew P. Juniper. "Learning RANS Model Parameters From LES Using Bayesian Inference." In ASME Turbo Expo 2023: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2023. http://dx.doi.org/10.1115/gt2023-102159.

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Abstract We propose a formal mathematical approach to assimilate LES data into values of RANS model parameters combined with some prior knowledge of the expected RANS parameter values. This is achieved using Bayesian inference to determine parameter values that maximize their posterior probability and is known as maximum a posteriori (MAP) estimation. We apply this approach to a premixed turbulent methane-air round jet flame using unburnt mixture equivalence ratio and bulk flow velocity as design parameters. The k-ε model is used for turbulence closure and the eddy dissipation concept (EDC) model is used to model combustion. Three dimesional LES data for six design cases are computed and upto three of these are used for MAP estimation. The likelihood of RANS solutions is evaluated using flow field statistics from LES at training data points. The results show significant improvement in agreement between LES and RANS solutions, computed using MAP estimate parameters for species mass fraction and temperature fields at design points not in the training set. Marginal improvement is observed for velocity fields. This is most likely due to the absence of production terms in the RANS model that capture the three-dimensional nature of the flow being modelled. The marginal likelihood of the RANS model when assimilating both k-ε and EDC model parameters is significantly higher than the case that leaves out the EDC model parameters. This suggests that the former approach is more likely to yield reliable RANS parameters. These results demonstrate the viability of MAP estimation as a means to improving the reliability of turbulent reacting flow RANS simulations for engineering design and optimization applications.
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Zhang, Jiacheng, Yang Liu, Huanbo Luan, Jingfang Xu, and Maosong Sun. "Prior Knowledge Integration for Neural Machine Translation using Posterior Regularization." In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/p17-1139.

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Reports on the topic "A Priori and a Posteriori Knowledge"

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Bhushan, Shanti, Greg Burgreen, Wesley Brewer, and Ian Dettwiller. Assessment of neural network augmented Reynolds averaged Navier Stokes turbulence model in extrapolation modes. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49702.

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A machine-learned model enhances the accuracy of turbulence transport equations of RANS solver and applied for periodic hill test case. The accuracy is investigated in extrapolation modes. A parametric study is also performed to understand the effect of network hyperparameters on training and model accuracy and to quantify the uncertainty in model accuracy due to the non-deterministic nature of the neural network training. For any network, less than optimal mini-batch size results in overfitting, and larger than optimal reduces accuracy. Data clustering is an efficient approach to prevent the machine-learned model from over training on more prevalent flow regimes, and results in a model with similar accuracy. Turbulence production is correlated with shear strain in the free-shear region, with shear strain and wall-distance and local velocity-based Reynolds number in the boundary layer regime, and with streamwise velocity gradient in the accelerating flow regime. The flow direction is key in identifying flow separation and reattachment regime. Machine-learned models perform poorly in extrapolation mode. A priori tests reveal model predictability improves as the hill dataset is partially added during training in a partial extrapolation model. These also provide better turbulent kinetic energy and shear stress predictions than RANS in a posteriori tests. Before a machine-learned model is applied for a posteriori tests, a priori tests should be performed.
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Klumpp, John, Sara de Souza Zanotta Dumit, and Guthrie Miller. Biokinetic Models Estimating Rapid Diffusion Using a Priori Knowledge of Flow Rates and Compartment Volumes. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1785472.

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Sadowski, Dieter. Board-Level Codetermination in Germany - The Importance and Economic Impact of Fiduciary Duties. Association Inter-University Centre Dubrovnik, 2021. http://dx.doi.org/10.53099/ntkd4304.

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The empirical accounts of the costs and benefits of quasi-parity codetermined supervisory boards, a very special German institution, have long been inconclusive. A valid economic analysis of a particular legal regulation must take the legal specificities seriously, otherwise it will be easily lost in economic fictions of functional equivalence. At its core the corporate actor “supervisory board” has no a priori objective function to be maximised – the corner stone of the theory of the firm – but its objective function will only be brought about a posteriori – should negotiations result in an agreement (E. Fraenkel). With this understanding,the paper presents six recent quasi-experimental studies on the economic (dis) advantageousness of the German codetermination laws that try to follow the rules of causal inference despite the lack of random variation. By and large they refute the hold-up model of codetermination by showing positive or nonnegative effects even on shareholder wealth – and a far-reaching improvement of the well-being of the core workforce. In conclusion, indications are offered that the shareholder primacy movement has only weakened, but not dissolved the “Deutschland AG”.
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Ceylan, Ismail Ilkan, Stefan Borgwardt, and Thomas Lukasiewicz. Most Probable Explanations for Probabilistic Database Queries. Technische Universität Dresden, 2017. http://dx.doi.org/10.25368/2023.220.

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Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.
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Bauer, Andrew, Jennifer Abras, and Nathan Hariharan. In situ and post-processing volume rendering with with Cinema. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40502.

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We present a new batch volume rendering technique which alleviates the time and expertise needed by the domain scientist in order to produce quality volume rendered results. This process can be done both in situ and as a post-processing step. The advantage of performing this as an in situ process is that the user is not required to have a priori knowledge of the exact physics and how best to create a transfer function to volume render that physics during the in situ run. For the post-processing use case, the user has the ability to easily examine a wide variety of transfer functions without the tedious work of manually generating each one.
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Guo, Ge, Jiageng Liu, and Guangheng Liu. Multi-sensor Fusion-based Vehicle Localization. SAE International, 2024. http://dx.doi.org/10.4271/epr2024023.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Multi-sensor fusion (MSF) is believed to be a promising tool for vehicular localization in urban environments. Due to the differences in principles and performance of various onboard vehicle sensors, MSF inevitably suffers from heterogeneous sources and vulnerability to cyber-attacks. Therefore, an essential requirement of MSF is the capability of providing a consumer-grade solution that operates in real-time, is accurate, and immune to abnormal conditions with guaranteed performance and quality of service for location-based applications. In other words, an MSF algorithm depends heavily on data synchronization, cost, an accurate process model, a priori knowledge of covariance matrices, integrity assessments, and security against cyber-attacks.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;b&gt;Multi-sensor Fusion-based Vehicle Localization&lt;/b&gt; addresses trending technologies in MSF-based vehicle localization and outlines some insights into the unsettled issues and their potential solutions. The discussions and outlook are presented as a collection of key topics, including multi-sensor measurement data processing, sensory selection, filtering, integrity assessment, and cybersecurity.&lt;/div&gt;&lt;div class="htmlview paragraph"&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt;Click here to access the full SAE EDGE&lt;/a&gt;&lt;sup&gt;TM&lt;/sup&gt;&lt;a href="https://www.sae.org/publications/edge-research-reports" target="_blank"&gt; Research Report portfolio.&lt;/a&gt;&lt;/div&gt;&lt;/div&gt;
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Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

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The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
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