Relevant bibliographies by topics / Bayesian framework

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian framework'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Bayesian framework"

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Dasgupta, Anirban, George Casella, Mohan Delampady, Christian Genest, William E. Strawderman, and Herman Rubin. "Correlation in a Bayesian framework." Canadian Journal of Statistics 28, no. 4 (December 2000): 675–87. http://dx.doi.org/10.2307/3315910.

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Zhang, Rui, and Ling Guan. "A Bayesian Image Retrieval Framework." International Journal of Digital Library Systems 1, no. 2 (2010): 43–58. http://dx.doi.org/10.4018/jdls.2010040103.

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Hicks, Tyler, Liliana Rodríguez-Campos, and Jeong Hoon Choi. "Bayesian Posterior Odds Ratios." American Journal of Evaluation 39, no. 2 (May 23, 2017): 278–89. http://dx.doi.org/10.1177/1098214017704302.

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To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.
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Calvetti, Daniela, and Erkki Somersalo. "Hypermodels in the Bayesian imaging framework." Inverse Problems 24, no. 3 (May 23, 2008): 034013. http://dx.doi.org/10.1088/0266-5611/24/3/034013.

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Modrak, Ryan T., Stephen J. Arrowsmith, and Dale N. Anderson. "A Bayesian framework for infrasound location." Geophysical Journal International 181, no. 1 (April 2010): 399–405. http://dx.doi.org/10.1111/j.1365-246x.2010.04499.x.

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Grzywacz, Norberto M., and Rosario M. Balboa. "A Bayesian Framework for Sensory Adaptation." Neural Computation 14, no. 3 (March 1, 2002): 543–59. http://dx.doi.org/10.1162/089976602317250898.

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Adaptation allows biological sensory systems to adjust to variations in the environment and thus to deal better with them. In this article, we propose a general framework of sensory adaptation. The underlying principle of this framework is the setting of internal parameters of the system such that certain prespecified tasks can be performed optimally. Because sensorial inputs vary probabilistically with time and biological mechanisms have noise, the tasks could be performed incorrectly. We postulate that the goal of adaptation is to minimize the number of task errors. This minimization requires prior knowledge of the environment and of the limitations of the mechanisms processing the information. Because these processes are probabilistic, we formulate the minimization with a Bayesian approach. Application of this Bayesian framework to the retina is successful in accounting for a host of experimental findings.
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Jbabdi, S., M. W. Woolrich, J. L. R. Andersson, and T. E. J. Behrens. "A Bayesian framework for global tractography." NeuroImage 37, no. 1 (August 2007): 116–29. http://dx.doi.org/10.1016/j.neuroimage.2007.04.039.

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Turkoz, Mehmet, Sangahn Kim, Young-Seon Jeong, Myong K. (MK) Jeong, Elsayed A. Elsayed, Khalifa N. Al-Khalifa, and Abdel Magid Hamouda. "Bayesian framework for fault variable identification." Journal of Quality Technology 51, no. 4 (October 30, 2018): 375–91. http://dx.doi.org/10.1080/00224065.2018.1507561.

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Calvetti, Daniela, Jari P. Kaipio, and Erkki Somersalo. "Inverse problems in the Bayesian framework." Inverse Problems 30, no. 11 (October 29, 2014): 110301. http://dx.doi.org/10.1088/0266-5611/30/11/110301.

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DelSole, Timothy. "A Bayesian Framework for Multimodel Regression." Journal of Climate 20, no. 12 (June 15, 2007): 2810–26. http://dx.doi.org/10.1175/jcli4179.1.

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Abstract This paper presents a framework based on Bayesian regression and constrained least squares methods for incorporating prior beliefs in a linear regression problem. Prior beliefs are essential in regression theory when the number of predictors is not a small fraction of the sample size, a situation that leads to overfitting—that is, to fitting variability due to sampling errors. Under suitable assumptions, both the Bayesian estimate and the constrained least squares solution reduce to standard ridge regression. New generalizations of ridge regression based on priors relevant to multimodel combinations also are presented. In all cases, the strength of the prior is measured by a parameter called the ridge parameter. A “two-deep” cross-validation procedure is used to select the optimal ridge parameter and estimate the prediction error. The proposed regression estimates are tested on the Development of a European Multimodel Ensemble System for Seasonal to Interannual Prediction (DEMETER) hindcasts of seasonal mean 2-m temperature over land. Surprisingly, none of the regression models proposed here can consistently beat the skill of a simple multimodel mean, despite the fact that one of the regression models recovers the multimodel mean in a suitable limit. This discrepancy arises from the fact that methods employed to select the ridge parameter are themselves sensitive to sampling errors. It is plausible that incorporating the prior belief that regression parameters are “large scale” can reduce overfitting and result in improved performance relative to the multimodel mean. Despite this, results from the multimodel mean demonstrate that seasonal mean 2-m temperature is predictable for at least three months in several regions.
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Dissertations / Theses on the topic "Bayesian framework"

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Tenenbaum, Joshua B. (Joshua Brett) 1972. "A Bayesian framework for concept learning." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/16714.

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Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Brain and Cognitive Sciences, 1999.
Includes bibliographical references (p. 297-314).
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Human concept learning presents a version of the classic problem of induction, which is made particularly difficult by the combination of two requirements: the need to learn from a rich (i.e. nested and overlapping) vocabulary of possible concepts and the need to be able to generalize concepts reasonably from only a few positive examples. I begin this thesis by considering a simple number concept game as a concrete illustration of this ability. On this task, human learners can with reasonable confidence lock in on one out of a billion billion billion logically possible concepts, after seeing only four positive examples of the concept, and can generalize informatively after seeing just a single example. Neither of the two classic approaches to inductive inference hypothesis testing in a constrained space of possible rules and computing similarity to the observed examples can provide a complete picture of how people generalize concepts in even this simple setting. This thesis proposes a new computational framework for understanding how people learn concepts from examples, based on the principles of Bayesian inference. By imposing the constraints of a probabilistic model of the learning situation, the Bayesian learner can draw out much more information about a concept's extension from a given set of observed examples than either rule-based or similarity-based approaches do, and can use this information in a rational way to infer the probability that any new object is also an instance of the concept. There are three components of the Bayesian framework: a prior probability distribution over a hypothesis space of possible concepts; a likelihood function, which scores each hypothesis according to its probability of generating the observed examples; and the principle of hypothesis averaging, under which the learner computes the probability of generalizing a concept to new objects by averaging the predictions of all hypotheses weighted by their posterior probability (proportional to the product of their priors and likelihoods). The likelihood, under the assumption of randomly sampled positive examples, embodies the size principle for scoring hypotheses: smaller consistent hypotheses are more likely than larger hypotheses, and they become exponentially more likely as the number of observed examples increases. The principle of hypothesis averaging allows the Bayesian framework to accommodate both rule-like and similarity-like generalization behavior, depending on how peaked the posterior probability is. Together, the size principle plus hypothesis averaging predict a convergence from similarity-like generalization (due to a broad posterior distribution) after very few examples are observed to rule-like generalization (due to a sharply peaked posterior distribution) after sufficiently many examples have been observed. The main contributions of this thesis are as follows. First and foremost, I show how it is possible for people to learn and generalize concepts from just one or a few positive examples (Chapter 2). Building on that understanding, I then present a series of case studies of simple concept learning situations where the Bayesian framework yields both qualitative and quantitative insights into the real behavior of human learners (Chapters 3-5). These cases each focus on a different learning domain. Chapter 3 looks at generalization in continuous feature spaces, a typical representation of objects in psychology and machine learning with the virtues of being analytically tractable and empirically accessible, but the downside of being highly abstract and artificial. Chapter 4 moves to the more natural domain of learning words for categories of objects and shows the relevance of the same phenomena and explanatory principles introduced in the more abstract setting of Chapters 1-3 for real-world learning tasks like this one. In each of these domains, both similarity-like and rule-like generalization emerge as special cases of the Bayesian framework in the limits of very few or very many examples, respectively. However, the transition from similarity to rules occurs much faster in the word learning domain than in the continuous feature space domain. I propose a Bayesian explanation of this difference in learning curves that places crucial importance on the density or sparsity of overlapping hypotheses in the learner's hypothesis space. To test this proposal, a third case study (Chapter 5) returns to the domain of number concepts, in which human learners possess a more complex body of prior knowledge that leads to a hypothesis space with both sparse and densely overlapping components. Here, the Bayesian theory predicts and human learners produce either rule-based or similarity-based generalization from a few examples, depending on the precise examples observed. I also discusses how several classic reasoning heuristics may be used to approximate the much more elaborate computations of Bayesian inference that this domain requires. In each of these case studies, I confront some of the classic questions of concept learning and induction: Is the acquisition of concepts driven mainly by pre-existing knowledge or the statistical force of our observations? Is generalization based primarily on abstract rules or similarity to exemplars? I argue that in almost all instances, the only reasonable answer to such questions is, Both. More importantly, I show how the Bayesian framework allows us to answer much more penetrating versions of these questions: How does prior knowledge interact with the observed examples to guide generalization? Why does generalization appear rule-based in some cases and similarity-based in others? Finally, Chapter 6 summarizes the major contributions in more detailed form and discusses how this work ts into the larger picture of contemporary research on human learning, thinking, and reasoning.
by Joshua B. Tenenbaum.
Ph.D.
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Denton, Stephen E. "Exploring active learning in a Bayesian framework." [Bloomington, Ind.] : Indiana University, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3380073.

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Thesis (Ph.D.)--Indiana University, Dept. of Psychological and Brain Sciences the Dept. of Cognitive Science, 2009.
Title from PDF t.p. (viewed on Jul 19, 2010). Source: Dissertation Abstracts International, Volume: 70-12, Section: B, page: 7870. Advisers: John K. Kruschke; Jerome R. Busemeyer.
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Scotto, Di Perrotolo Alexandre. "A Theoretical Framework for Bayesian Optimization Convergence." Thesis, KTH, Optimeringslära och systemteori, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-225129.

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Bayesian optimization is a well known class of derivative-free optimization algorithms mainly used for expensive black-box objective functions. Despite their efficiency, they suffer from a lack of rigorous convergence criterion which makes them more prone to be used as modeling tools rather than optimizing tools. This master thesis proposes, analyzes, and tests a globally convergent framework (that is to say the convergence to a stationary point regardless the initial sample) for Bayesian optimization algorithms. The framework design intends to preserve the global search characteristics for minimum while being rigorously monitored to converge.
Bayesiansk optimering är en välkänd klass av globala optimeringsalgoritmer som inte beror av derivator och främst används för optimering av dyra svartlådsfunktioner. Trots sin relativa effektivitet lider de av en brist av stringent konvergenskriterium som gör dem mer benägna att användas som modelleringsverktyg istället för som optimeringsverktyg. Denna rapport är avsedd att föreslå, analysera och testa en ett globalt konvergerande ramverk (på ett sätt som som beskrivs vidare) för Bayesianska optimeringsalgoritmer, som ärver de globala sökegenskaperna för minimum medan de noggrant övervakas för att konvergera.
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Zhong, Xionghu. "Bayesian framework for multiple acoustic source tracking." Thesis, University of Edinburgh, 2010. http://hdl.handle.net/1842/4752.

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Acoustic source (speaker) tracking in the room environment plays an important role in many speech and audio applications such as multimedia, hearing aids and hands-free speech communication and teleconferencing systems; the position information can be fed into a higher processing stage for high-quality speech acquisition, enhancement of a specific speech signal in the presence of other competing talkers, or keeping a camera focused on the speaker in a video-conferencing scenario. Most of existing systems focus on the single source tracking problem, which assumes one and only one source is active all the time, and the state to be estimated is simply the source position. However, in practical scenarios, multiple speakers may be simultaneously active, and the tracking algorithm should be able to localise each individual source and estimate the number of sources. This thesis contains three contributions towards solutions to multiple acoustic source tracking in a moderate noisy and reverberant environment. The first contribution of this thesis is proposing a time-delay of arrival (TDOA) estimation approach for multiple sources. Although the phase transform (PHAT) weighted generalised cross-correlation (GCC) method has been employed to extract the TDOAs of multiple sources, it is primarily used for a single source scenario and its performance for multiple TDOA estimation has not been comprehensively studied. The proposed approach combines the degenerate unmixing estimation technique (DUET) and GCC method. Since the speech mixtures are assumed window-disjoint orthogonal (WDO) in the time-frequency domain, the spectrograms can be separated by employing DUET, and the GCC method can then be applied to the spectrogram of each individual source. The probabilities of detection and false alarm are also proposed to evaluate the TDOA estimation performance under a series of experimental parameters. Next, considering multiple acoustic sources may appear nonconcurrently, an extended Kalman particle filtering (EKPF) is developed for a special multiple acoustic source tracking problem, namely “nonconcurrent multiple acoustic tracking (NMAT)”. The extended Kalman filter (EKF) is used to approximate the optimum weights, and the subsequent particle filtering (PF) naturally takes the previous position estimates as well as the current TDOA measurements into account. The proposed approach is thus able to lock on the sharp change of the source position quickly, and avoid the tracking-lag in the general sequential importance resampling (SIR) PF. Finally, these investigations are extended into an approach to track the multiple unknown and time-varying number of acoustic sources. The DUET-GCC method is used to obtain the TDOA measurements for multiple sources and a random finite set (RFS) based Rao-blackwellised PF is employed and modified to track the sources. Each particle has a RFS form encapsulating the states of all sources and is capable of addressing source dynamics: source survival, new source appearance and source deactivation. A data association variable is defined to depict the source dynamic and its relation to the measurements. The Rao-blackwellisation step is used to decompose the state: the source positions are marginalised by using an EKF, and only the data association variable needs to be handled by a PF. The performances of all the proposed approaches are extensively studied under different noisy and reverberant environments, and are favorably comparable with the existing tracking techniques.
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Kwee, Ivo Widjaja. "Towards a Bayesian framework for optical tomography." Thesis, University College London (University of London), 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.325658.

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Anand, Farminder Singh. "Bayesian framework for improved R&D decisions." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/39530.

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This thesis work describes the formulation of a Bayesian approach along with new tools to systematically reduce uncertainty in Research&Development (R&D) alternatives. During the initial stages of R&D many alternatives are considered and high uncertainty exists for all the alternatives. The ideal approach in addressing the many R&D alternatives is to find the one alternative which is stochastically dominant i.e. the alternative which is better in all possible scenarios of uncertainty. Often a stochastically dominant alternative does not exist. This leaves the R&D manager with two alternatives, either to make a selection based on user defined utility function or to gather more information in order to reduce uncertainty in the various alternatives. From the decision makers perspective the second alternative has more intrinsic value, since reduction of uncertainty will improve the confidence in the selection and further reduce the high downside risk involved with the decisions made under high uncertainty. The motivation for this work is derived from our preliminary work on the evaluation of biorefiney alternatives, which brought into limelight the key challenges and opportunities in the evaluation of R&D alternatives. The primary challenge in the evaluation of many R&D alternatives was the presence of uncertainty in the many unit operations within each and every alternative. Additionally, limited or non-existent experimental data made it infeasible to quantify the uncertainty and lead to inability to develop an even simple systematic strategy to reduce it. Moreover, even if the uncertainty could be quantified, the traditional approaches (scenario analysis or stochastic analysis), lacked the ability to evaluate the key group of uncertainty contributors. Lastly, the traditional design of experiment approaches focus towards reduction in uncertainty in the parameter estimates of the model, whereas what is required is a design of experiment approach which focuses on the decision (selection of the key alternative). In order to tackle all the above mentioned challenges a Bayesian framework along with two new tools is proposed. The Bayesian framework consists of three main steps: a. Quantification of uncertainty b. Evaluation of key uncertainty contributors c. Design of experiment strategies, focussed on decision making rather than the traditional parameter uncertainty reduction To quantify technical uncertainty using expert knowledge, existing elicitation methods in the literature (outside chemical engineering domain) are used. To illustrate the importance of quantifying technical uncertainty, a bio-refinery case study is considered. The case study is an alternative for producing ethanol as a value added product in a Kraft mill producing pulp from softwood. To produce ethanol, a hot water pre-extraction of hemi-cellulose is considered, prior to the pulping stage. Using this case study, the methodology to quantify technical uncertainty using experts' knowledge is demonstrated. To limit the cost of R&D investment for selection or rejection of an R&D alternative, it is essential to evaluate the key uncertainty contributors. Global sensitivity analysis (GSA) is a tool which can be used to evaluate the key uncertainties. But quite often global sensitivity analysis fails to differentiate between the uncertainties and assigns them equal global sensitivity index. To counter this failing of GSA, a new method conditional global sensitivity (c-GSA) is presented, which is able to differentiate between the uncertainties even when GSA fails to do so. To demonstrate the value of c-GSA many small examples are presented. The third and the last key method in the Bayesian framework is the decision oriented design of experiment. Traditional 'Design of Experiment' (DOE) approaches focus on minimization of parameter error variance. In this work, a new "decision-oriented" DOE approach is proposed that takes into account how the generated data, and subsequently, the model developed based on them will be used in decision making. By doing so, the parameter variances get distributed in a manner such that its adverse impact on the targeted decision making is minimal. Results show that the new decision-oriented DOE approach significantly outperforms the standard D-optimal design approach. The new design method should be a valuable tool when experiments are conducted for the purpose of making R&D decisions. Finally, to demonstrate the importance of the overall Bayesian framework a bio-refinery case study is considered. The case study consists of the alternative to introduce a hemi-cellulose pre-extraction stage prior to pulping in a thermo-mechanical pulp mill. Application of the Bayesian framework to address this alternative, results in significant improvement in the prediction of the true potential value of the alternative.
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Shao, Yuan. "A Bayesian reasoning framework for model-driven vision." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284789.

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Brunton, Alan. "A Bayesian framework for panoramic imaging of complex scenes." Thesis, University of Ottawa (Canada), 2006. http://hdl.handle.net/10393/27336.

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This thesis presents a Bayesian framework for generating a panoramic image of a scene from a set of images, where there is only a small amount of overlap between adjacent images. Dense correspondence is computed using loopy belief propagation on a pair-wise Markov random field, and used to resample and blend the input images to remove artifacts in overlapping regions and seams along the overlap boundaries. Bayesian approaches have been used extensively in vision and imaging, and involve computing an observational likelihood from the input images and imposing a priori constraints. Photoconsistency or matching cost computed from the images is used as the likelihood in this thesis. The primary contribution of this thesis is the use of and efficient belief propagation algorithm to yield the piecewise smooth resampling of the input images with the highest probability of not producing artifacts or seams.
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Atrash, Amin. "A Bayesian Framework for Online Parameter Learning in POMDPs." Thesis, McGill University, 2011. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=104587.

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Decision-making under uncertainty has become critical as autonomous and semi-autonomous agents become more ubiquitious in our society. These agents must deal with uncertainty and ambiguity from the environment and still perform desired tasks robustly. Partially observable Markov decision processes (POMDPs) provide a principled mathematical framework for modelling agents operating in such an environment. These models are able to capture the uncertainty from noisy sensors, inaccurate actuators, and perform decision-making in light of the agent's incomplete knowledge of the world. POMDPs have been applied successfully in domains ranging from robotics to dialogue management to medical systems. Extensive research has been conducted on methods for optimizing policies for POMDPs. However, these methods typically assume a model of the environment is known. This thesis presents a Bayesian reinforcement learning framework for learning POMDP parameters during execution. This framework takes advantage of agents which work alongside an operator who can provide optimal policy information to help direct the learning. By using Bayesian reinforcement learning, the agent can perform learning concurrently with execution, incorporate incoming data immediately, and take advantage of prior knowledge of the world. By using such a framework, an agent is able to adapt its policy to that of the operator. This framework is validated on data collected from the interaction manager of an autonomous wheelchair. The interaction manager acts as an intelligent interface between the user and the robot, allowing the user to issue high-level commands through natural interface such as speech. This interaction manager is controlled using a POMDP and acts as a rich scenario for learning in which the agent must adjust to the needs of the user over time.
Comme le nombre d'agents autonomes et semi-autonomes dansnotre société ne cesse de croître, les prises de décisions sous incertitude constituent désormais un problème critique. Malgré l'incertitude et l'ambiguité inhérentes à leurs environnements, ces agents doivent demeurer robustes dans l'exécution de leurs tâches. Les processus de décision markoviens partiellement observables (POMDP) offrent un cadre mathématique permettant la modélisation des agents et de leurs environnements. Ces modèles sont capables de capturer l'incertitude due aux perturbations dans les capteurs ainsi qu'aux actionneurs imprécis. Ils permettent conséquemment une prise de décision tenant compte des connaissances imparfaites des agents. À ce jour, les POMDP ont été utilisés avec succès dans un éventail de domaines, allant de la robotique à la gestion de dialogue, en passant par la médecine. Plusieurs travaux de recherche se sont penchés sur des méthodes visant à optimiser les POMDP. Cependant, ces méthodes requièrent habituellement un modèle environnemental préalablement connu. Dans ce mémoire, une méthode bayésienne d'apprentissage par renforcement est présentée, avec laquelle il est possible d'apprendre les paramètres du modèle POMDP pendant l'éxécution. Cette méthode tire avantage d'une coopération avec un opérateur capable de guider l'apprentissage en divulguant certaines données optimales. Avec l'aide du renforcement bayésien, l'agent peut apprendre pendant l'éxécution, incorporer immédiatement les données nouvelles et profiter des connaissances précédentes, pour finalement pouvoir adapter sa politique de décision à celle de l'opérateur. La méthodologie décrite est validée à l'aide de données produites par le gestionnaire d'interactions d'une chaise roulante autonome. Ce gestionnaire prend la forme d'une interface intelligente entre le robot et l'usager, permettant à celui-ci de stipuler des commandes de haut niveau de façon naturelle, par exemple en parlant à voix haute. Les fonctions du gestionnaire sont accomplies à l'aide d'un POMDP et constituent un scénario d'apprentissage idéal, dans lequel l'agent doit s'ajuster progressivement aux besoins de l'usager.
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Sullivan, Josephine Jean. "A Bayesian framework for object localisation in visual images." Thesis, University of Oxford, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.365337.

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Books on the topic "Bayesian framework"

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Stübler, Sabine. Modelling Proteasome Dynamics in a Bayesian Framework. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-20167-8.

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Bazaldua, Diego A. Luna. Exploring Skill Condensation Rules for Cognitive Diagnostic Models in a Bayesian Framework. [New York, N.Y.?]: [publisher not identified], 2015.

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Chung, Meng-ta. Estimating the Q-matrix for Cognitive Diagnosis Models in a Bayesian Framework. [New York, N.Y.?]: [publisher not identified], 2014.

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Stübler, Sabine. Modelling Proteasome Dynamics in a Bayesian Framework. Springer Spektrum, 2017.

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Titelbaum, Michael G. Quitting Certainties: A Bayesian Framework Modeling Degrees of Belief. Oxford University Press, 2014.

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Quitting Certainties A Bayesian Framework Modeling Degrees Of Belief. Oxford University Press, USA, 2013.

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Titelbaum, Michael G. Quitting Certainties: A Bayesian Framework Modeling Degrees of Belief. Oxford University Press, Incorporated, 2012.

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Korrapati, Raghu B. A Bayesian Model Framework to Determine Patient Compliance in Glaucoma Cases. iUniverse, Inc., 2005.

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Yu, Angela J. Bayesian Models of Attention. Edited by Anna C. (Kia) Nobre and Sabine Kastner. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199675111.013.025.

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Traditionally, attentional selection has been thought of as arising naturally from resource limitations, with a focus on what might be the most apt metaphor, e.g. whether it is a ‘bottleneck’ or ‘spotlight’. However, these simple metaphors cannot account for the specificity, flexibility, and heterogeneity of the way attentional selection manifests itself in different behavioural contexts. A recent body of theoretical work has taken a different approach, focusing on the computational needs of selective processing, relative to environmental constraints and behavioural goals. They typically adopt a normative computational framework, incorporating Bayes-optimal algorithms for information processing and action selection. This chapter reviews some of this recent modelling work, specifically in the context of attention for learning, covert spatial attention, and overt spatial attention.
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Pearl, Lisa, and Sharon Goldwater. Statistical Learning, Inductive Bias, and Bayesian Inference in Language Acquisition. Edited by Jeffrey L. Lidz, William Snyder, and Joe Pater. Oxford University Press, 2016. http://dx.doi.org/10.1093/oxfordhb/9780199601264.013.28.

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Bayesian models of language acquisition are powerful tools for exploring how linguistic generalizations can be made. Notably, Bayesian models assume children leverage statistical information in sophisticated ways, and so it is important to demonstrate that children’s behavior is consistent with both the assumptions of the Bayesian framework and the predictions of specific models. We first provide a historical overview of behavioral evidence suggesting children utilize available statistical information to make useful generalizations in a variety of tasks. We then discuss the Bayesian modeling framework, including benefits of particular interest to both developmental and theoretical linguists. We conclude with a review of several case studies that demonstrate how Bayesian models can be applied to problems of interest in language acquisition.
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Book chapters on the topic "Bayesian framework"

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Pole, Andy, Mike West, and Jeff Harrison. "Methodological Framework." In Applied Bayesian Forecasting and Time Series Analysis, 13–27. Boston, MA: Springer US, 1994. http://dx.doi.org/10.1007/978-1-4899-3432-1_2.

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Almond, Russell G., Robert J. Mislevy, Linda S. Steinberg, Duanli Yan, and David M. Williamson. "The Conceptual Assessment Framework." In Bayesian Networks in Educational Assessment, 411–65. New York, NY: Springer New York, 2015. http://dx.doi.org/10.1007/978-1-4939-2125-6_12.

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Sitara, K., and S. Remya. "Image Deblurring Using Bayesian Framework." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 515–28. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-27317-9_52.

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Demoment, Guy, and Yves Goussard. "Inversion within the Probabilistic Framework." In Bayesian Approach to Inverse Problems, 59–78. London, UK: ISTE, 2010. http://dx.doi.org/10.1002/9780470611197.ch3.

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Alais, David, and David Burr. "Cue Combination Within a Bayesian Framework." In Multisensory Processes, 9–31. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10461-0_2.

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Hancock, Edwin R., and Marcello Pelillo. "A Bayesian Framework for Associative Memories." In Neural Nets WIRN VIETRI-96, 125–31. London: Springer London, 1997. http://dx.doi.org/10.1007/978-1-4471-0951-8_10.

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Cao, Zijun, Yu Wang, and Dianqing Li. "Bayesian Framework for Geotechnical Site Characterization." In Probabilistic Approaches for Geotechnical Site Characterization and Slope Stability Analysis, 53–62. Berlin, Heidelberg: Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-662-52914-0_3.

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Zhang, Rui, Kui Wu, Kim-Hui Yap, and Ling Guan. "A Collaborative Bayesian Image Annotation Framework." In Advances in Multimedia Information Processing - PCM 2008, 348–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89796-5_36.

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Friedman, Avner. "A Bayesian framework for computer vision." In Mathematics in Industrial Problems, 193–201. New York, NY: Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4615-7405-7_18.

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Stübler, Sabine. "Introduction." In Modelling Proteasome Dynamics in a Bayesian Framework, 17–32. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-20167-8_1.

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Conference papers on the topic "Bayesian framework"

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Fu, Shuai, and Nizar Bouguila. "A Bayesian Intrusion Detection Framework." In 2018 International Conference on Cyber Security and Protection of Digital Services (Cyber Security). IEEE, 2018. http://dx.doi.org/10.1109/cybersecpods.2018.8560681.

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Myers, K. J., and R. F. Wagner. "Bayesian framework for calculating observer performance." In OSA Annual Meeting. Washington, D.C.: Optica Publishing Group, 1992. http://dx.doi.org/10.1364/oam.1992.fcc1.

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The Bayesian or ideal observer is a construct from signal detection theory. By definition, the ideal observer uses all information present in an image to make optimal decisions regarding the underlying scene. By comparing the calculated performance of the ideal observer to the measured performance of a human observer, we can determine how well a displayed image is utilized by the human observer. Human performance has been shown to be highly efficient for certain, detection and discrimination tasks. Degradations in human performance due to correlated noise or uncertainty in the task have been explained by using this Bayesian framework. This talk will describe the ideal-observer decision function for various tasks and methods for calculating the ideal-observer SNR. Existing data comparing the ideal and human observers for a variety of visual tasks will be reviewed.
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Zhenbo Cheng, Wenfeng Chen, Tian Ran, Zhidong Deng, and Xiaolan Fu. "A Bayesian framework for crowding effect." In 2010 Chinese Control and Decision Conference (CCDC). IEEE, 2010. http://dx.doi.org/10.1109/ccdc.2010.5499009.

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Rui Zhang and Ling Guan. "A collaborative Bayesian image retrieval framework." In ICASSP 2009 - 2009 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2009. http://dx.doi.org/10.1109/icassp.2009.4959993.

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Lebeltel, O. "A Bayesian framework for robotic programming." In The twentieth international workshop on bayesian inference and maximum entropy methods in science and engineering. AIP, 2001. http://dx.doi.org/10.1063/1.1381923.

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Fredlund, Richard, Richard M. Everson, and Jonathan E. Fieldsend. "A Bayesian framework for active learning." In 2010 International Joint Conference on Neural Networks (IJCNN). IEEE, 2010. http://dx.doi.org/10.1109/ijcnn.2010.5596917.

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Huang, Shih-Shinh, Li-Chen Fu, and Pei-Yung Hsiao. "A Bayesian Framework for Foreground Segmentation." In 2006 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2006. http://dx.doi.org/10.1109/icsmc.2006.385021.

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Tesfamicael, Solomon, and Faraz Barzideh. "Clustered Compressed Sensing via Bayesian Framework." In 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim). IEEE, 2015. http://dx.doi.org/10.1109/uksim.2015.21.

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Dalton, Lori A., and Edward R. Dougherty. "Optimal classifiers within a Bayesian framework." In 2012 IEEE Statistical Signal Processing Workshop (SSP). IEEE, 2012. http://dx.doi.org/10.1109/ssp.2012.6319760.

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WEBB-ROBERTSON, B. M., S. L. HAVRE, and D. A. PAYNE. "A BAYESIAN FRAMEWORK FOR SNP IDENTIFICATION." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2004. http://dx.doi.org/10.1142/9789812702456_0040.

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Reports on the topic "Bayesian framework"

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Brown, Jesse, Goran Arbanas, Dorothea Wiarda, and Andrew Holcomb. Bayesian Optimization Framework for Imperfect Data or Models. Office of Scientific and Technical Information (OSTI), June 2022. http://dx.doi.org/10.2172/1874643.

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Chiang, A., and S. Ford. BayesMT: A Probabilistic Bayesian Framework for the Seismic Moment Tensor. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1890801.

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Ye, Ming. Computational Bayesian Framework for Quantification and Reduction of Predictive Uncertainty in Subsurface Environmental Modeling. Office of Scientific and Technical Information (OSTI), January 2019. http://dx.doi.org/10.2172/1491235.

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Glimm, James, Yunha Lee, Kenny Q. Ye, and David H. Sharp. Prediction Using Numerical Simulations, A Bayesian Framework for Uncertainty Quantification and its Statistical Challenge. Fort Belvoir, VA: Defense Technical Information Center, January 2002. http://dx.doi.org/10.21236/ada417842.

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Hossain, Niamat Ullah Ibne, Raed Jaradat, Seyedmohsen Hosseini, Mohammad Marufuzzaman, and Randy Buchanan. A framework for modeling and assessing system resilience using a Bayesian network : a case study of an interdependent electrical infrastructure systems. Engineer Research and Development Center (U.S.), April 2021. http://dx.doi.org/10.21079/11681/40299.

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This research utilizes Bayesian network to address a range of possible risks to the electrical power system and its interdependent networks (EIN) and offers possible options to mitigate the consequences of a disruption. The interdependent electrical infrastructure system in Washington, D.C. is used as a case study to quantify the resilience using the Bayesian network. Quantification of resilience is further analyzed based on different types of analysis such as forward propagation, backward propagation, sensitivity analysis, and information theory. The general insight drawn from these analyses indicate that reliability, backup power source, and resource restoration are the prime factors contributed towards enhancing the resilience of an interdependent electrical infrastructure system.
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Johannesson, Gardar, Vera Bulaevskaya, Abe Ramirez, Sean Ford, and Artie Rodgers. A Bayesian inversion framework for yield and height-of-burst/depth-of-burial for near-surface explosions. Office of Scientific and Technical Information (OSTI), September 2015. http://dx.doi.org/10.2172/1226968.

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Maupin, Kathryn, Anh Tran, William Lewis, Patrick Knapp, V. Joseph, Michael Glinsky, and Sonata Valaitis. Towards Z-Next: The Integration of Theory, Experiments, and Computational Simulation in a Bayesian Data Assimilation Framework. Office of Scientific and Technical Information (OSTI), September 2022. http://dx.doi.org/10.2172/1891191.

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Johannesson, G., and S. Myers. A Bayesian Framework for Locating Seismic Events Using Absolute Arrival Time Data along with Back Azimuth and Slowness Observations. Office of Scientific and Technical Information (OSTI), August 2014. http://dx.doi.org/10.2172/1165775.

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Tong, C., J. Morgan, A. Chinen, C. Anderson-Cook, J. Carroll, C. Saha, B. Omell, et al. Development of a framework for sequential Bayesian design of experiments: Application to a pilot-scale solvent-based CO2 capture process. Office of Scientific and Technical Information (OSTI), November 2021. http://dx.doi.org/10.2172/1871778.

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Baltagi, Badi H., Georges Bresson, Anoop Chaturvedi, and Guy Lacroix. Robust dynamic space-time panel data models using ε-contamination: An application to crop yields and climate change. CIRANO, January 2023. http://dx.doi.org/10.54932/ufyn4045.

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This paper extends the Baltagi et al. (2018, 2021) static and dynamic ε-contamination papers to dynamic space-time models. We investigate the robustness of Bayesian panel data models to possible misspecification of the prior distribution. The proposed robust Bayesian approach departs from the standard Bayesian framework in two ways. First, we consider the ε-contamination class of prior distributions for the model parameters as well as for the individual effects. Second, both the base elicited priors and the ε-contamination priors use Zellner (1986)’s g-priors for the variance-covariance matrices. We propose a general “toolbox” for a wide range of specifications which includes the dynamic space-time panel model with random effects, with cross-correlated effects `a la Chamberlain, for the Hausman-Taylor world and for dynamic panel data models with homogeneous/heterogeneous slopes and cross-sectional dependence. Using an extensive Monte Carlo simulation study, we compare the finite sample properties of our proposed estimator to those of standard classical estimators. We illustrate our robust Bayesian estimator using the same data as in Keane and Neal (2020). We obtain short run as well as long run effects of climate change on corn producers in the United States.
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