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WIREs Computational Statistics, 2009
AbstractBayesian networks are defined, and the chain rule for Bayesian networks is stated. Outlines of algorithms provided: inference in Bayesian networks, sensitivity analysis, EM for parameter learning, and learning structure. Copyright © 2009 John Wiley & Sons, Inc.This article is categorized under:Statistical and Graphical Methods of Data ...
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AbstractBayesian networks are defined, and the chain rule for Bayesian networks is stated. Outlines of algorithms provided: inference in Bayesian networks, sensitivity analysis, EM for parameter learning, and learning structure. Copyright © 2009 John Wiley & Sons, Inc.This article is categorized under:Statistical and Graphical Methods of Data ...
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2012
This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC ...
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This chapter provides an overview of the Bayesian approach to data analysis, modeling, and statistical decision making. The topics covered go from basic concepts and definitions (random variables, Bayes' rule, prior distributions) to various models of general use in biology (hierarchical models, in particular) and ways to calibrate and use them (MCMC ...
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Philosophy of Science, 2013
A piece of folklore enjoys some currency among philosophical Bayesians, according to which Bayesian agents who, intuitively speaking, spread their credence over the entire space of available hypotheses are certain to converge to the truth. The goals of the current discussion are to show that that kernel of truth in this folklore is in some ways fairly ...
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A piece of folklore enjoys some currency among philosophical Bayesians, according to which Bayesian agents who, intuitively speaking, spread their credence over the entire space of available hypotheses are certain to converge to the truth. The goals of the current discussion are to show that that kernel of truth in this folklore is in some ways fairly ...
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2004
AbstractThis article is concerned with Bayesian epistemology. Bayesianism claims to provide a unified theory of epistemic and practical rationality based on the principle of mathematical expectation. In its epistemic guise, it requires believers to obey the laws of probability.
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AbstractThis article is concerned with Bayesian epistemology. Bayesianism claims to provide a unified theory of epistemic and practical rationality based on the principle of mathematical expectation. In its epistemic guise, it requires believers to obey the laws of probability.
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Technometrics, 2004
trol charts, and their relationship to process capability. Chapter 7, “Preparing for Control Charts,” presents procedures for determining sample size and frequency, with some comparison of variable and attribute control charts.
A. Gelman +5 more
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trol charts, and their relationship to process capability. Chapter 7, “Preparing for Control Charts,” presents procedures for determining sample size and frequency, with some comparison of variable and attribute control charts.
A. Gelman +5 more
semanticscholar +1 more source
Biometrics, 1991
As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models.
Dixon, Dennis O., Simon, Richard
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As a means of assessing the importance of variation in treatment effect among patient subsets, we derived posterior distributions for subset-specific treatment effects. The effects are represented by combinations of terms for treatment and treatment-by-covariate interaction effects in familiar regression models.
Dixon, Dennis O., Simon, Richard
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Journal of the Royal Statistical Society. Series A (General), 1984
Unter den Basisannahmen für Gruppenentscheidungen: Kohärenz und Rationalität der Individuen, Suche und Nutzung einer Gruppen- Nutzenfunktion, aggregierte Erwartungen hängen nicht vom konkreten Spiel ab, wird eine Darstellung der Gruppenerwartung \(p=\prod^{n}_{i=1}p_ i^{a_ i}\) gegeben. Als Konsequenzen ergeben sich ein Maß für die Nichtübereinstimmung
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Unter den Basisannahmen für Gruppenentscheidungen: Kohärenz und Rationalität der Individuen, Suche und Nutzung einer Gruppen- Nutzenfunktion, aggregierte Erwartungen hängen nicht vom konkreten Spiel ab, wird eine Darstellung der Gruppenerwartung \(p=\prod^{n}_{i=1}p_ i^{a_ i}\) gegeben. Als Konsequenzen ergeben sich ein Maß für die Nichtübereinstimmung
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Philosophy of Science, 2016
Say that an agent is epistemically humble if she is less than fully confident that her opinions will converge to the truth, given appropriate evidence. Is such humility rationally permissible? According to Gordon Belot’s orgulity argument: the answer is yes, but long-run convergence-to-the-truth theorems force Bayesians to answer no.
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Say that an agent is epistemically humble if she is less than fully confident that her opinions will converge to the truth, given appropriate evidence. Is such humility rationally permissible? According to Gordon Belot’s orgulity argument: the answer is yes, but long-run convergence-to-the-truth theorems force Bayesians to answer no.
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Bayesian reaction optimization as a tool for chemical synthesis
Nature, 2021Benjamin J Shields +2 more
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