Results 311 to 320 of about 2,535,602 (380)

The logarithmic memristor-based Bayesian machine. [PDF]

Commun Eng
Turck C, Harabi KE, Pontlevy A, Ballet T, Hirtzlin T, Vianello E, Laurent R, Droulez J, Bessière P, Bocquet M, Portal JM, Querlioz D.   +11 more
europepmc   +1 more source

Bayesian Workflow for Generative Modeling in Computational Psychiatry. [PDF]

Comput Psychiatr
Hess AJ, Iglesias S, Köchli L, Marino S, Müller-Schrader M, Rigoux L, Mathys C, Harrison OK, Heinzle J, Frässle S, Stephan KE.   +10 more
europepmc   +1 more source

A tutorial on bayesian multiple-group comparisons of latent growth curve models with count distributed variables. [PDF]

Behav Res Methods
Bendler J, Reinecke J.
europepmc   +1 more source

Enhanced Bayesian model for multienvironmental selection of winter hybrids maize: assessing grain yield using 'ProbBreed'. [PDF]

Plant Methods
Basnet B, Kunwar CB, Upreti U.
europepmc   +1 more source

Establishment of a machine learning model for predicting splenic hilar lymph node metastasis. [PDF]

NPJ Digit Med
Ishizu K, Takahashi S, Kouno N, Takasawa K, Takeda K, Matsui K, Nishino M, Hayashi T, Yamagata Y, Matsui S, Yoshikawa T, Hamamoto R.   +11 more
europepmc   +1 more source

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Bayesian probability

Synthese, 2009
Bayesian decision theory is here construed as explicating a particular concept of rational choice and Bayesian probability is taken to be the concept of probability used in that theory. Bayesian probability is usually identified with the agent’s degrees of belief but that interpretation makes Bayesian decision theory a poor explication of the relevant ...
P. Maher
semanticscholar   +3 more sources

Predictions of nuclear charge radii and physical interpretations based on the naive Bayesian probability classifier

, 2020
Background: The nuclear charge radii provide direct information for the nuclear structures. In recent years, many pioneering researches have been devoted to the nuclear charge radii based on the Bayesian neural networks (BNN) method.Purpose: The neural ...
Yunfei Ma, Chen Su, Jian Liu, Z. Ren, Xu, Yonghao Gao   +5 more
semanticscholar   +1 more source

A Bayesian probability network

AIP Conference Proceedings, 1986
A model of an associative neural network is developed in which the state of each node is described by a probability density. The realization of the network is based on the pairwise joint probabilities obtained from a training set of states. A positive definite ‘‘energy’’ functional of the probabilities may be constructed from Bayes’ rule of statistical
E. Abrahams, C. H. Anderson
openaire   +2 more sources

Constructing Site-Specific Multivariate Probability Distribution Model Using Bayesian Machine Learning

Journal of engineering mechanics, 2019
This study proposes a novel data-driven Bayesian machine learning method for constructing site-specific multivariate probability distribution models in geotechnical engineering.
J. Ching, K. Phoon
semanticscholar   +1 more source