Results 21 to 30 of about 1,995,230 (269)
Probability biases as Bayesian inference [PDF]
Judgment and Decision Making, 2006 In this article, I will show how several observed biases in human probabilistic reasoning can be partially explained as good heuristics for making inferences in an environment where probabilities have uncertainties associated to them.
André C. R. Martins
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Improved naive Bayesian probability classifier in predictions of nuclear mass
Physical Review C, 2021 Background: Recently, novel statistical methods such as neural networks and Bayesian learning methods are implemented to describe the nuclear masses. Purpose: Based on previous studies, an improved naive Bayesian probability (iNBP) classifier is proposed
Yifan Liu +5 more
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暴雨灾害, 2021 Using the historical precipitation observation data in the Haihe River Basin and the ECMWF ensemble prediction, the 289 grid points in the Haihe River Basin are modeled with Bayesian Processor of Output (BPO), which revise the determine precipitation ...
Shu XU, Mingming XIONG, Fajing CHEN
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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?
Sanborn, Adam N., Chater, Nick
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The research on Bayesian inference for geophysical inversion
地球与行星物理论评, 2022 Based on statistical theory, the Bayesian inversion method adopts the posterior probability distribution to evaluate the model parameters under the constraints of prior information and observation data.
Xingda Jiang, Wei Zhang, Hui Yang
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Quantum probabilities as Bayesian probabilities [PDF]
Physical Review A, 2002 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. In this paper we show that, despite being prescribed by a fundamental law, probabilities for individual quantum systems can be understood within the Bayesian approach.
Caves, Carlton M. +2 more
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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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Bayesian optimization for computationally extensive probability distributions. [PDF]
PLoS ONE, 2018 An efficient method for finding a better maximizer of computationally extensive probability distributions is proposed on the basis of a Bayesian optimization technique.
Ryo Tamura, Koji Hukushima
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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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