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Quantum probabilities as Bayesian probabilities [PDF]
Physical Review A, 2001 In the Bayesian approach to probability theory, probability quantifies a degree of belief for a single trial, without any a priori connection to limiting frequencies.
A. Einstein +15 more
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A categorical foundation for Bayesian probability [PDF]
Applied Categorical Structures, 2013 Given two measurable spaces $H$ and $D$ with countably generated $\sigma$-algebras, a perfect prior probability measure $P_H$ on $H$ and a sampling distribution $S: H \rightarrow D$, there is a corresponding inference map $I: D \rightarrow H$ which is ...
A Kock +13 more
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ON BAYESIAN LOGICAL PROBABILITY [PDF]
ETS Research Bulletin Series, 1964 ABSTRACTIn a recent paper (Edwards, Lindman, and Savage, 1963), psychologists have been urged to adopt a Bayesian personal probability approach to statistical inference. The purpose of this paper is to suggest that a Bayesian logical probability approach may be superior to the personal probability approach.
Melvin R. Novick
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Probability and Bayesian Statistics. [PDF]
Journal of the Royal Statistical Society. Series A (Statistics in Society), 1989 P. M. Lee, Reinhard Karl Wolfgang Viertl
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Bayesian Brains without Probabilities [PDF]
Trends in Cognitive Sciences, 2016 Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities?
Nick Chater, Adam N. Sanborn
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The Bayesian Estimate of Vector Autoregressive Model Parameters Adopt Informative Prior Information
Tikrit Journal of Pure Science, 2023 This research included the bayesian estimate for vector Autoregressive model with rank (p) in addition to statistical tests and predict Bayesian when the random error of model followed generalized multivariate modified Bessel distribution.
Haifaa Abdulgawwad Saeed +2 more
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A Bayesian Inference Based Computational Tool for Parametric and Nonparametric Medical Diagnosis
Diagnostics, 2023 Medical diagnosis is the basis for treatment and management decisions in healthcare. Conventional methods for medical diagnosis commonly use established clinical criteria and fixed numerical thresholds. The limitations of such an approach may result in a
Theodora Chatzimichail +1 more
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Risk prediction and diagnosis of urban gas pipeline accidents based on polymorphic fuzzy Bayesian network [PDF]
Journal of Hebei University of Science and Technology, 2023 In order to evaluate the risk level of the urban gas pipeline system, and provide the reference for follow-up prevention efforts, a quantitative analysis method of gas pipeline accident risk was proposed based on polymorphic fuzzy Bayesian network ...
Ying QU +3 more
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Computational probability modeling and Bayesian inference [PDF]
Revue Africaine de Recherche en Informatique et Mathématiques Appliquées, 2008 Computational probabilistic modeling and Bayesian inference has met a great success over the past fifteen years through the development of Monte Carlo methods and the ever increasing performance of computers. Through methods such as Monte Carlo Markov chain and sequential Monte Carlo Bayesian inference effectively combines with Markovian modelling ...
Campillo, Fabien +2 more
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Consistency of Bayesian Linear Model Selection With a Growing Number of Parameters [PDF]
, 2011 Linear models with a growing number of parameters have been widely used in modern statistics. One important problem about this kind of model is the variable selection issue. Bayesian approaches, which provide a stochastic search of informative variables,
Clayton, Murray K., Shang, Zuofeng
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