Готові списки джерел за темами / Prior informatif

Добірка наукової літератури з теми "Prior informatif"

Автор: Grafiati
Опубліковано: 21 червня 2022

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Зміст

  1. Статті в журналах
  2. Дисертації
  3. Книги
  4. Частини книг
  5. Тези доповідей конференцій
  6. Звіти організацій

Статті в журналах з теми "Prior informatif":

1

Hasanah, Uswatul, Ferra Yanuar, and Dodi Devianto. "PENDUGAAN PARAMETER PADA DISTRIBUSI GAMMA DENGAN METODE BAYES." Jurnal Matematika UNAND 7, no. 4 (February 19, 2019): 81. http://dx.doi.org/10.25077/jmu.7.4.81-86.2018.

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Penelitian ini membahas tentang pendugaan parameter pada distribusi Gamma dengan parameter α diketahui. Metode pendugaan parameter yang digunakan adalah metode Bayes dengan dua distribusi prior, yaitu distribusi prior konjugat dan distribusi prior non-informatif. Distribusi prior konjugat yang diperoleh adalah distribusi Gamma (α , , β, ) dan distribusi prior non-informatif diperoleh dengan melakukan metode perluasan Jeffrey sehingga menghasilkan prior Jeffrey adalah 1 β2k .Kata Kunci: Metode Bayes, Distribusi prior, Metode Jeffrey, Distribusi Gamma
2

Rahmadiah, Annisa. "INFERENSI BAYESIAN PADA DISTRIBUSI EKSPONENSIAL." Jurnal Matematika UNAND 7, no. 4 (February 19, 2019): 93. http://dx.doi.org/10.25077/jmu.7.4.93-99.2018.

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Penelitian ini dilakukan dengan tujuan menduga parameter pada distribusi Eksponensial dengan metode Bayes. Pendugaan parameter dilakukan secara analitik dengan menggunakan distribusi Gamma sebagai prior konjugat dan distribusi prior Jeffrey sebagai prior non-informatif. Setelah itu, dilakukan evaluasi penduga menggunakan metode AIC. Berdasarkan studi analitik diperoleh bahwa distribusi Gamma sebagai distribusi prior konjugat lebih baik dibandingkan dengan prior Jeffrey dalam hal pendugaan parameter.Kata Kunci: Metode Bayes, distribusi prior, distribusi posterior, fungsi likelihood, metode AIC
3

Yani, Resti Nanda, Ferra Yanuar, and Hazmira Yozza. "INFERENSI BAYESIAN UNTUK 2 DARI DISTRIBUSI NORMAL DENGAN BERBAGAI DISTRIBUSI PRIOR." Jurnal Matematika UNAND 7, no. 2 (May 1, 2018): 132. http://dx.doi.org/10.25077/jmu.7.2.132-139.2018.

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Abstrak. Pada penelitian ini dilakukan pendugaan parameter variansi (2) dari dis-tribusi Normal dengan mean () diketahui. Pendugaan parameter variansi (2) terse-but dilakukan secara analitik dengan menggunakan distribusi Invers Gamma sebagaiprior konjugat, metode Jerey sebagai prior non-informatif dan distribusi Uniform se-bagai prior non-konjugat. Pada penelitian ini kriteria evaluasi penduga yang digunakanadalah MSE dan sifat tak bias. Berdasarkan studi analitik diperoleh bahwa distribusiInvers Gamma sebagai prior konjugat merupakan prior terbaik diantara dua distribusiprior lainnya.Kata Kunci: Inferensi statistika, metode Bayes, distribusi prior, fungsi likelihood, dis-tribusi Normal, Invers Gamma, metode Jerey, distribusi Uniform
4

Febriani, Dini, Sugito Sugito, and Alan Prahutama. "ANALISIS METODE BAYESIAN MENGGUNAKAN NON-INFORMATIF PRIOR UNIFORM DISKRIT PADA SISTEM ANTREAN PELAYANAN GERBANG TOL MUKTIHARJO." Jurnal Gaussian 10, no. 3 (December 30, 2021): 337–45. http://dx.doi.org/10.14710/j.gauss.v10i3.32783.

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The growth rate of the traffic that is high resulting in congestion on the road network system. One of the government's efforts in addressing the issue with the build highways to reduce congestion, especially in large cities. One of the queuing phenomena that often occurs in the city of Semarang is the queue at the Toll Gate Muktiharjo, that the queue of vehicles coming to make toll payment. This study aims to determine how the service system at the Toll Gate Muktiharjo. This can be known by getting a queue system model and a measure of system performance from the distribution of arrival and service. The distribution of arrival and service are determined by finding the posterior distribution using the Bayesian method. The bayesian method combine the likelihood function of the sample and the prior distribution. The likelihood function is a negative binomial. The prior distribution used a uniform discrete. Based on the calculations and analysis, it can be concluded that the queueing system model at the Toll Gate Muktiharjo is a (Beta/Beta/5):(GD/∞/∞). The queue simulation obtained that the service system Toll Gate Muktiharjo is optimal based on the size of the system performance because busy probability is higher than jobless probability.
5

Fu, Ying, Xi Wu, Xiaohua Li, Kun He, Yi Zhang, and Jiliu Zhou. "Image Motion Restoration Using Fractional-Order Gradient Prior." Informatica 26, no. 4 (January 1, 2015): 621–34. http://dx.doi.org/10.15388/informatica.2015.67.

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6

Hyun, Jeong-Hoon. "주관적 성과평가에서 전년도 성과정보가 관대화경향에 미치는 영향". Korean Governmental Accounting Review 19, № 1 (30 квітня 2021): 167–218. http://dx.doi.org/10.15710/kgar.2021.19.1.167.

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7

Pandey, Vijay Kumar, Rajeev Pandey, and Mayank Trivedi. "Bayesian Method in Linear Model and Constant Time Series Model Using Non-Informative Prior Under Phenology." Mathematical Journal of Interdisciplinary Sciences 3, no. 2 (March 30, 2015): 183–91. http://dx.doi.org/10.15415/mjis.2015.32016.

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8

CHANDRA, N., and V. K. RATHAUR. "Bayesian Estimation of Augmented Exponential Strength Reliability Models Under Non-informative Priors." Mathematical Journal of Interdisciplinary Sciences 5, no. 1 (September 5, 2016): 15–31. http://dx.doi.org/10.15415/mjis.2016.51002.

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9

Myungjin Cho, Myungjin Cho. "Three-dimensional color photon counting microscopy using Bayesian estimation with adaptive priori information." Chinese Optics Letters 13, no. 7 (2015): 070301–70304. http://dx.doi.org/10.3788/col201513.070301.

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10

Kalaylioglu, Zeynep, and Haydar Demirhan. "A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements." Statistical Methods in Medical Research 26, no. 6 (November 6, 2015): 2885–96. http://dx.doi.org/10.1177/0962280215615003.

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Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.
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Статті в журналах Дисертації Книги

Дисертації з теми "Prior informatif":

1

Papoutsis, Panayotis. "Potentiel et prévision des temps d'attente pour le covoiturage sur un territoire." Thesis, Ecole centrale de Nantes, 2021. http://www.theses.fr/2021ECDN0059.

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Cette thèse s’intéresse au potentiel et à la prévision des temps d’attente concernant le covoiturage sur un territoire en utilisant des méthodes d’apprentissage statistique. Cinq thèmes principaux sont abordés dans le présent manuscrit. Le premier présente des techniques de régression quantile afin de prédire des temps d’attente. Le deuxième détaille la construction d’un processus de travail empruntant des outils des Systèmes d’Information Géographique (SIG) afin d’exploiter pleinement les données issues du covoiturage. Dans un troisième temps nous construisons un modèle hiérarchique bayésien en vue de prédire des flux de trafic et des temps d’attente. En quatrième partie nous proposons une méthode de construction d’une loi a priori informative par transfert bayésien dans le but d’améliorer les prédictions des temps d’attente pour une situation de jeu de données court. Enfin, le dernier thème se concentre sur la mise en production et l’exploitation industrielle du modèle hiérarchique bayésien
This thesis focuses on the potential and prediction of carpooling waiting times in a territory using statistical learning methods. Five main themes are covered in this manuscript. The first presents quantile regression techniques to predict waiting times. The second details the construction of a workflow based on Geographic Information Systems (GIS) tools in order to fully leverage the carpooling data. In a third part we develop a hierarchical bayesian model in order to predict traffic flows and waiting times. In the fourth part, we propose a methodology for constructing an informative prior by bayesian transfer to improve the prediction of waiting times for a short dataset situation. Lastly, the final theme focuses on the production and industrial exploitation of the bayesian hierarchical model
2

Bioche, Christèle. "Approximation de lois impropres et applications." Thesis, Clermont-Ferrand 2, 2015. http://www.theses.fr/2015CLF22626/document.

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Le but de cette thèse est d’étudier l’approximation d’a priori impropres par des suites d’a priori propres. Nous définissons un mode de convergence sur les mesures de Radon strictement positives pour lequel une suite de mesures de probabilité peut admettre une mesure impropre pour limite. Ce mode de convergence, que nous appelons convergence q-vague, est indépendant du modèle statistique. Il permet de comprendre l’origine du paradoxe de Jeffreys-Lindley. Ensuite, nous nous intéressons à l’estimation de la taille d’une population. Nous considérons le modèle du removal sampling. Nous établissons des conditions nécessaires et suffisantes sur un certain type d’a priori pour obtenir des estimateurs a posteriori bien définis. Enfin, nous montrons à l’aide de la convergence q-vague, que l’utilisation d’a priori vagues n’est pas adaptée car les estimateurs obtenus montrent une grande dépendance aux hyperparamètres
The purpose of this thesis is to study the approximation of improper priors by proper priors. We define a convergence mode on the positive Radon measures for which a sequence of probability measures could converge to an improper limiting measure. This convergence mode, called q-vague convergence, is independant from the statistical model. It explains the origin of the Jeffreys-Lindley paradox. Then, we focus on the estimation of the size of a population. We consider the removal sampling model. We give necessary and sufficient conditions on the hyperparameters in order to have proper posterior distributions and well define estimate of abundance. In the light of the q-vague convergence, we show that the use of vague priors is not appropriate in removal sampling since the estimates obtained depend crucially on hyperparameters
3

Pohl, Kilian Maria. "Prior information for brain parcellation." Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/33925.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2005.
Includes bibliographical references (p. 171-184).
To better understand brain disease, many neuroscientists study anatomical differences between normal and diseased subjects. Frequently, they analyze medical images to locate brain structures influenced by disease. Many of these structures have weakly visible boundaries so that standard image analysis algorithms perform poorly. Instead, neuroscientists rely on manual procedures, which are time consuming and increase risks related to inter- and intra-observer reliability [53]. In order to automate this task, we develop an algorithm that robustly segments brain structures. We model the segmentation problem in a Bayesian framework, which is applicable to a variety of problems. This framework employs anatomical prior information in order to simplify the detection process. In this thesis, we experiment with different types of prior information such as spatial priors, shape models, and trees describing hierarchical anatomical relationships. We pose a maximum a posteriori probability estimation problem to find the optimal solution within our framework. From the estimation problem we derive an instance of the Expectation Maximization algorithm, which uses an initial imperfect estimate to converge to a good approximation.
(cont.) The resulting implementation is tested on a variety of studies, ranging from the segmentation of the brain into the three major brain tissue classes, to the parcellation of anatomical structures with weakly visible boundaries such as the thalamus or superior temporal gyrus. In general, our new method performs significantly better than other :standard automatic segmentation techniques. The improvement is due primarily to the seamless integration of medical image artifact correction, alignment of the prior information to the subject, detection of the shape of anatomical structures, and representation of the anatomical relationships in a hierarchical tree.
by Kilian Maria Pohl.
Ph.D.
4

Ahmed, Syed Ejaz Carleton University Dissertation Mathematics. "Estimation strategies under uncertain prior information." Ottawa, 1987.

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5

Sunmola, Funlade Tajudeen. "Optimising learning with transferable prior information." Thesis, University of Birmingham, 2013. http://etheses.bham.ac.uk//id/eprint/3983/.

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This thesis addresses the problem of how to incorporate user knowledge about an environment, or information acquired during previous learning in that environment or a similar one, to make future learning more effective. The problem is tackled within the framework of learning from rewards while acting in a Markov Decision Process (MDP). Appropriately incorporating user knowledge and prior experience into learning should lead to better performance during learning (the exploitation-exploration trade-off), and offer a better solution at the end of the learning period. We work in a Bayesian setting and consider two main types of transferable information namely historical data and constraints involving absolute and relative restrictions on process dynamics. We present new algorithms for reasoning with transition constraints and show how to revise beliefs about the MDP transition matrix using constraints and prior knowledge. We also show how to use the resulting beliefs to control exploration. Finally we demonstrate benefits of historical information via power priors and by using process templates to transfer information from one environment to a second with related local process dynamics. We present results showing that incorporating historical data and constraints on state transitions in uncertain environments, either separately or collectively, can improve learning performance.
6

Ren, Shijie. "Using prior information in clinical trial design." Thesis, University of Sheffield, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.555104.

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A current concern in medical research is low productivity In the pharmaceutical industry. Failure rates of Phase III clinical trials are high, and this is very costly in terms of using resources and money. Our aim in this thesis is to incorporate prior information in clinical trial design and develop better assessments of the chances of successful clinical trials, so that trial sponsors can improve their success rates. Assurance calculations, which take into account uncertainty about how effective the treatment actually is, provide a more reliable assessment of the probability of a successful trial outcome comparing with power calculations. We develop assurance methods to accommodate survival outcome measures, assuming both parametric and nonparametric models. We also develop prior elicitation procedures for each survival model so that the assurance calculations can be performed more easily and reliably. Prior elicitation is not an easy task, and we may be uncertain about what distribution 'best' represents an expert's beliefs. We demonstrated that robustness of the assurance to different choices of prior distribution can be assessed by treating the elicitation process as a Bayesian inference problem, using a nonparametric Bayesian approach to quantify uncertainty in the expert's density function of the true treatment effect. In this thesis, we also consider a decision-making problem for a single-arm open label Phase 11 trial for the PhD sponsor Roche. Based on the Bayesian decision- theoretic approach and assurance calculations, a model is developed for the trial sponsor in finding the optimal trial strategies according to their beliefs about the true treatment effect.
7

Parsley, M. P. "Simultaneous localisation and mapping with prior information." Thesis, University College London (University of London), 2011. http://discovery.ucl.ac.uk/1318103/.

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This thesis is concerned with Simultaneous Localisation and Mapping (SLAM), a technique by which a platform can estimate its trajectory with greater accuracy than odometry alone, especially when the trajectory incorporates loops. We discuss some of the shortcomings of the "classical" SLAM approach (in particular EKF-SLAM), which assumes that no information is known about the environment a priori. We argue that in general this assumption is needlessly stringent; for most environments, such as cities some prior information is known. We introduce an initial Bayesian probabilistic framework which considers the world as a hierarchy of structures, and maps (such as those produced by SLAM systems) as consisting of features derived from them. Common underlying structure between features in maps allows one to express and thus exploit geometric relations between them to improve their estimates. We apply the framework to EKF-SLAM for the case of a vehicle equipped with a range-bearing sensor operating in an urban environment, building up a metric map of point features, and using a prior map consisting of line segments representing building footprints. We develop a novel method called the Dual Representation, which allows us to use information from the prior map to not only improve the SLAM estimate, but also reduce the severity of errors associated with the EKF. Using the Dual Representation, we investigate the effect of varying the accuracy of the prior map for the case where the underlying structures and thus relations between the SLAM map and prior map are known. We then generalise to the more realistic case, where there is "clutter" - features in the environment that do not relate with the prior map. This involves forming a hypothesis for whether a pair of features in the SLAMstate and prior map were derived from the same structure, and evaluating this based on a geometric likelihood model. Initially we try an incrementalMultiple Hypothesis SLAM(MHSLAM) approach to resolve hypotheses, developing a novel method called the Common State Filter (CSF) to reduce the exponential growth in computational complexity inherent in this approach. This allows us to use information from the prior map immediately, thus reducing linearisation and EKF errors. However we find that MHSLAM is still too inefficient, even with the CSF, so we use a strategy that delays applying relations until we can infer whether they apply; we defer applying information from structure hypotheses until their probability of holding exceeds a threshold. Using this method we investigate the effect of varying degrees of "clutter" on the performance of SLAM.
8

Viggh, Herbert E. M. "Surface Prior Information Reflectance Estimation (SPIRE) algorithms." Thesis, Massachusetts Institute of Technology, 2001. http://hdl.handle.net/1721.1/17564.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2001.
Includes bibliographical references (p. 393-396).
In this thesis we address the problem of estimating changes in surface reflectance in hyperspectral image cubes, under unknown multiplicative and additive illumination noise. Rather than using the Empirical Line Method (ELM) or physics-based approaches, we assumed the presence of a prior reflectance image cube and ensembles of typical multiplicative and additive illumination noise vectors, and developed algorithms which estimate reflectance using this prior information. These algorithms were developed under the additional assumptions that the illumination effects were band limited to lower spatial frequencies and that the differences in the surface reflectance from the prior were small in area relative to the scene, and have defined edges. These new algorithms were named Surface Prior Information Reflectance Estimation (SPIRE) algorithms. Spatial SPIRE algorithms that employ spatial processing were developed for six cases defined by the presence or absence of the additive noise, and by whether or not the noise signals are spatially uniform or varying. These algorithms use high-pass spatial filtering to remove the noise effects. Spectral SPIRE algorithms that employ spectral processing were developed and use zero-padded Principal Components (PC) filtering to remove the illumination noise. Combined SPIRE algorithms that use both spatial and spectral processing were also developed. A Selective SPIRE technique that chooses between Combined and Spectral SPIRE reflectance estimates was developed; it maximizes estimation performance on both modified and unmodified pixels. The different SPIRE algorithms were tested on HYDICE airborne sensor hyperspectral data, and their reflectance estimates were compared to those from the physics-based ATmospheric REMoval (ATREM) and the Empirical Line Method atmospheric compensation algorithms. SPIRE algorithm performance was found to be nearly identical to the ELM ground-truth based results. SPIRE algorithms performed better than ATREM overall, and significantly better under high clouds and haze. Minimum-distance classification experiments demonstrated SPIRE's superior performance over both ATREM and ELM in cross-image supervised classification applications. The taxonomy of SPIRE algorithms was presented and suggestions were made concerning which SPIRE algorithm is recommended for various applications.
by Herbert Erik Mattias Viggh.
Ph.D.
9

Ghadermarzy, Navid. "Using prior support information in compressed sensing." Thesis, University of British Columbia, 2013. http://hdl.handle.net/2429/44912.

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Compressed sensing is a data acquisition technique that entails recovering estimates of sparse and compressible signals from n linear measurements, significantly fewer than the signal ambient dimension N. In this thesis we show how we can reduce the required number of measurements even further if we incorporate prior information about the signal into the reconstruction algorithm. Specifically, we study certain weighted nonconvex Lp minimization algorithms and a weighted approximate message passing algorithm. In Chapter 1 we describe compressed sensing as a practicable signal acquisition method in application and introduce the generic sparse approximation problem. Then we review some of the algorithms used in compressed sensing literature and briefly introduce the method we used to incorporate prior support information into these problems. In Chapter 2 we derive sufficient conditions for stable and robust recovery using weighted Lp minimization and show that these conditions are better than those for recovery by regular Lp and weighted L1. We present extensive numerical experiments, both on synthetic examples and on audio, and seismic signals. In Chapter 3 we derive weighted AMP algorithm which iteratively solves the weighted L1 minimization. We also introduce a reweighting scheme for weighted AMP algorithms which enhances the recovery performance of weighted AMP. We also apply these algorithms on synthetic experiments and on real audio signals.
10

Liu, Yang. "Application of prior information to discriminative feature learning." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/285558.

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Learning discriminative feature representations has attracted a great deal of attention since it is a critical step to facilitate the subsequent classification, retrieval and recommendation tasks. In this dissertation, besides incorporating prior knowledge about image labels into the image classification as most prevalent feature learning methods currently do, we also explore some other general-purpose priors and verify their effectiveness in the discriminant feature learning. As a more powerful representation can be learned by implementing such general priors, our approaches achieve state-of-the-art results on challenging benchmarks. We elaborate on these general-purpose priors and highlight where we have made novel contributions. We apply sparsity and hierarchical priors to the explanatory factors that describe the data, in order to better discover the data structure. More specifically, in the first approach we propose that we only incorporate sparse priors into the feature learning. To this end, we present a support discrimination dictionary learning method, which finds a dictionary under which the feature representation of images from the same class have a common sparse structure while the size of the overlapped signal support of different classes is minimised. Then we incorporate sparse priors and hierarchical priors into a unified framework, that is capable of controlling the sparsity of the neuron activation in deep neural networks. Our proposed approach automatically selects the most useful low-level features and effectively combines them into more powerful and discriminative features for our specific image classification problem. We also explore priors on the relationships between multiple factors. When multiple independent factors exist in the image generation process and only some of them are of interest to us, we propose a novel multi-task adversarial network to learn a disentangled feature which is optimized with respect to the factor of interest to us, while being distraction factors agnostic. When common factors exist in multiple tasks, leveraging common factors cannot only make the learned feature representation more robust, but also enable the model to generalise from very few labelled samples. More specifically, we address the domain adaptation problem and propose the re-weighted adversarial adaptation network to reduce the feature distribution divergence and adapt the classifier from source to target domains.
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Книги з теми "Prior informatif":

1

Shavell, Steven. Acquisition and disclosure of information prior to economic exchange. [Cambridge: Harvard Law School, 1991.

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2

Machiels-Bongaerts, Maureen. Mobilizing prior knowledge in text processing: The selective-attention hypothesis versus the cognitive set-point hypothesis. [Maastricht]: Universitaire Pers Maastricht, 1993.

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3

Ontario Council of Regents for Colleges of Applied Arts and Technology. Prior Learning Assessment Steering Committee. Information systems for prior learning assessment support: Discussion paper. [North Bay, Ont: Canadore College, 1994.

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4

United States. President's National Security Telecommunications Advisory Committee. Issue review: A review of NSTAC issues addressed prior to NSTAC XIX. [Washington, D.C.?: The Committee, 1997.

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5

Office, General Accounting. Government consultants: Agencies' consulting services contract obligations for fiscal year 1987 : fact sheet for the Honorable David Pryor, Chairman, Subcommittee on Federal Services, Post Office, and Civil Service, Committee on Governmental Affairs, U.S. Senate. Washington, D.C: The Office, 1988.

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6

Golan, Amos. Prior Information. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780199349524.003.0008.

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In this chapter I introduce and quantify prior information and show how to incorporate it into the info-metrics framework. The priors developed arise from fundamental properties of the system, from logical reasoning, or from empirical observations. I start the chapter with the derivation of priors for discrete distributions, which can be handled via the grouping property, and a detailed derivation of surprisal analysis. Constructing priors for continuous distributions is more challenging. That problem is tackled via the method of transformation groups, which is related to the mathematical concept of group theory. That method works for both discrete and continuous functions. The last approaches I discuss are based on empirical information. The close relationship between priors, treatment effects, and score functions is discussed and demonstrated in the last section. Visual illustrations of the theory and numerous theoretical and applied examples are provided.
7

Venegas-Martinez, Francisco. Some studies on information measures and prior distributions. 1988.

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8

Prado, Raquel. Multistate models for mental fatigue. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.29.

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This article discusses the use of structured, multivariate Bayesian dynamic models in the analysis of experimental data involving large-scale electroencephalography (EEG) signals or time series generated on individuals subject to tasks inducing mental fatigue. It first provides an overview of the goals and challenges in the analysis of brain signals, using the EEG case as example, before describing the development and application of novel time-varying autoregressive and regime switching models, which incorporate relevant prior information via structured priors and fitted using novel, customized Bayesian computational methods. In the experiment, a subject was asked to perform simple arithmetic operations for a period of three hours. Prior to the experiment, the subject was confirmed to be alert. After the experiment ended, the subject was fatigued. The study demonstrates that Bayesian analysis is useful for real time detection of cognitive fatigue.
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K, Srull Thomas, Wyer Robert S, and Smith Eliot R, eds. Content and process specificity in the effects of prior experiences. Hillsdale, N.J: Lawrence Erlbaum Associates, 1990.

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10

Yanagisawa, Yasuo. Kenetsu hoso: Senji janarizumu shishi. Keyaki Shuppan, 1995.

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Частини книг з теми "Prior informatif":

1

Blasco, Agustín. "Prior Information." In Bayesian Data Analysis for Animal Scientists, 193–211. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54274-4_9.

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2

Lambert, Paul B. "Prior Information Conditions." In Essential Introduction to Understanding European Data Protection Rules, 143–50. Boca Raton : CRC Press, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781138069848-10.

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Lambert, Paul B. "Prior Information Conditions." In Essential Introduction to Understanding European Data Protection Rules, 143–50. Boca Raton : CRC Press, 2017.: Auerbach Publications, 2017. http://dx.doi.org/10.1201/9781315115269-10.

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Robert, Christian P. "From Prior Information to Prior Distributions." In Springer Texts in Statistics, 89–135. New York, NY: Springer New York, 1994. http://dx.doi.org/10.1007/978-1-4757-4314-2_3.

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Walter, Gero, and Frank P. A. Coolen. "Sets of Priors Reflecting Prior-Data Conflict and Agreement." In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 153–64. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40596-4_14.

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O’Donnell, R. T., A. E. Nicholson, B. Han, K. B. Korb, M. J. Alam, and L. R. Hope. "Causal Discovery with Prior Information." In Lecture Notes in Computer Science, 1162–67. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11941439_141.

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Berger, James O. "Prior Information and Subjective Probability." In Springer Series in Statistics, 74–117. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4757-4286-2_3.

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Feng, Qinrong, and Duoqian Miao. "Structured Prior Knowledge and Granular Structures." In Brain Informatics, 115–26. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04954-5_22.

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John, Jacob, and Prabu Sevugan. "Image Dehazing Through Dark Channel Prior and Color Attenuation Prior." In Communications in Computer and Information Science, 147–59. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88244-0_15.

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Xie, Boyi, and Rebecca J. Passonneau. "Supervised HDP Using Prior Knowledge." In Natural Language Processing and Information Systems, 197–202. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31178-9_21.

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Тези доповідей конференцій з теми "Prior informatif":

1

Chen, Yuanfang, Noel Crespi, Lin Lv, Mingchu Li, Antonio M. Ortiz, and Lei Shu. "Locating using prior information." In SIGCOMM'13: ACM SIGCOMM 2013 Conference. New York, NY, USA: ACM, 2013. http://dx.doi.org/10.1145/2486001.2491688.

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Parsley, Martin P., and Simon J. Julier. "Exploiting prior information in GraphSLAM." In 2011 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2011. http://dx.doi.org/10.1109/icra.2011.5979628.

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3

von Borries, R., C. Jacques Miosso, and C. Potes. "Compressed Sensing Using Prior Information." In 2007 2nd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing. IEEE, 2007. http://dx.doi.org/10.1109/camsap.2007.4497980.

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4

"Optimizing ICA Using Prior Information." In The First International Workshop on Biosignal Processing and Classification. SciTePress - Science and and Technology Publications, 2005. http://dx.doi.org/10.5220/0001195800270034.

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5

Snoussi, Hichem. "Information geometry and prior selection." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 22nd International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2003. http://dx.doi.org/10.1063/1.1570549.

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6

Shergadwala, Murtuza N., and Jitesh H. Panchal. "Human Inductive Biases in Design Decision Making." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22252.

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Abstract Designers make information acquisition decisions, such as where to search and when to stop the search. Such decisions are typically made sequentially, such that at every search step designers gain information by learning about the design space. However, when designers begin acquiring information, their decisions are primarily based on their prior knowledge. Prior knowledge influences the initial set of assumptions that designers use to learn about the design space. These assumptions are collectively termed as inductive biases. Identifying such biases can help us better understand how designers use their prior knowledge to solve problems in the light of uncertainty. Thus, in this study, we identify inductive biases in humans in sequential information acquisition tasks. To do so, we analyze experimental data from a set of behavioral experiments conducted in the past [1–5]. All of these experiments were designed to study various factors that influence sequential information acquisition behaviors. Across these studies, we identify similar decision making behaviors in the participants in their very first decision to “choose x”. We find that their choices of “x” are not uniformly distributed in the design space. Since such experiments are abstractions of real design scenarios, it implies that further contextualization of such experiments would only increase the influence of these biases. Thus, we highlight the need to study the influence of such biases to better understand designer behaviors. We conclude that in the context of Bayesian modeling of designers’ behaviors, utilizing the identified inductive biases would enable us to better model designer’s priors for design search contexts as compared to using non-informative priors.
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Trnka, Pavel, and Vladimir Havlena. "Subspace identification method incorporating prior information." In 2007 46th IEEE Conference on Decision and Control. IEEE, 2007. http://dx.doi.org/10.1109/cdc.2007.4434236.

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Ravana, Sri Devi, Laurence A. Park, and Alistair Moffat. "System scoring using partial prior information." In the 32nd international ACM SIGIR conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1571941.1572129.

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Jiang, Bo, Ming Li, and Ravi Tandon. "Local Information Privacy with Bounded Prior." In ICC 2019 - 2019 IEEE International Conference on Communications (ICC). IEEE, 2019. http://dx.doi.org/10.1109/icc.2019.8761176.

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Day, Peter S., and Peter Bladon. "Using prior information to enhance tracking." In Defense and Security, edited by Michael K. Masten and Larry A. Stockum. SPIE, 2004. http://dx.doi.org/10.1117/12.543623.

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Звіти організацій з теми "Prior informatif":

1

ROJAS, Temistocles, Vasily DEMYANOV, Mike CHRISTIE, and Dan ARNOLD. Use of Geological Prior Information in Reservoir. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0093.

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Baumeister, Christiane, and James Hamilton. Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information. Cambridge, MA: National Bureau of Economic Research, December 2014. http://dx.doi.org/10.3386/w20741.

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3

Hughett, Paul William. Algorithms for biomagnetic source imaging with prior anatomical and physiological information. Office of Scientific and Technical Information (OSTI), December 1995. http://dx.doi.org/10.2172/195677.

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4

Hughett, P. Tradeoffs between measurement residual and reconstruction error in inverse problems with prior information. Office of Scientific and Technical Information (OSTI), June 1995. http://dx.doi.org/10.2172/106621.

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5

Marthaler, Daniel, Andrea L. Bertozzi, and Ira B. Schwartz. Levy Searches Based on A Priori Information: The Biased Levy Walk. Fort Belvoir, VA: Defense Technical Information Center, January 2004. http://dx.doi.org/10.21236/ada638319.

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Vessella, Robert L. Does the Phenotyping of Disseminated Prostate Cancer Cells in Blood and Bone Marrow Prior to Radical Prostatectomy Provide Prognostic Information? Fort Belvoir, VA: Defense Technical Information Center, July 2004. http://dx.doi.org/10.21236/ada435227.

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Vessella, Robert L. Does the Phenotyping of Disseminated Prostate Cancer Cells in Blood and Bone Marrow Prior to Radical Prostatectomy Provide Prognostic Information? Fort Belvoir, VA: Defense Technical Information Center, July 2002. http://dx.doi.org/10.21236/ada412293.

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Vessella, Robert. Does the Phenotyping of Disseminated Prostate Cancer Cells in Blood and Bone Marrow Prior to Radical Prostatectomy Provide Prognostic Information? Fort Belvoir, VA: Defense Technical Information Center, July 2003. http://dx.doi.org/10.21236/ada418201.

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Henseler, Sean P. Addressing the Legal Challenges of Network Centric Warfare. Case In Point: The Legal Implications of Obtaining an Information and Knowledge Advantage" Prior to Hostilities". Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada389662.

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Kharel, Arjun, Sudhir Shrestha, Sadikshya Bhattarai, Pauline Oosterhoff, and Karen Snyder. Assessment of Outreach and Engagement with Prospective Migrants by the Agencies Recruiting Labourers for Foreign Employment. Institute of Development Studies, May 2022. http://dx.doi.org/10.19088/ids.2022.037.

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This study was conducted to identify the gaps in policies and practices of labour recruitment in Nepal and assess the outreach and engagement of major formal labour intermediaries, private recruitment agencies (PRAs) and pre-departure orientation training (PDOT) centres, with migrant workers for providing information on human trafficking prior to departure. The study used data from interviews with the management of 15 PRAs and 10 PDOT centres, along with a review of online materials published by the sampled PRAs and PDOT centres and existing publications on labour migration from Nepal.

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