Results 31 to 40 of about 2,394,910 (364)
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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A Probability-based Evolutionary Algorithm with Mutations to Learn Bayesian Networks [PDF]
International Journal of Interactive Multimedia and Artificial Intelligence, 2014 Bayesian networks are regarded as one of the essential tools to analyze causal relationship between events from data. To learn the structure of highly-reliable Bayesian networks from data as quickly as possible is one of the important problems that ...
Sho Fukuda +2 more
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Objective and Subjective Solomonoff Probabilities in Quantum Mechanics [PDF]
Electronic Proceedings in Theoretical Computer Science, 2018 Algorithmic probability has shown some promise in dealing with the probability problem in the Everett interpretation, since it provides an objective, single-case probability measure.
Allan F. Randall
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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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Measurement: Sensors, 2022 The process of revising the Guide to the Expression of Uncertainty in Measurement (GUM) is ongoing. A successful revision must be theoretically sound, so it must be based on a recognized paradigm for scientific data analysis.
R. Willink
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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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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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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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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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A Bayesian definition of ‘most probable’ parameters [PDF]
Geotechnical Research, 2018 Since guidelines for choosing ‘most probable’ parameters in ground engineering design codes are vague, concerns are raised regarding their definition, as well as the associated uncertainties. This paper introduces Bayesian inference for a new rigorous approach to obtaining the estimates of the most probable parameters based on observations collected ...
Giovanna Biscontin +2 more
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