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Computationally efficient Bayesian inference for semi-parametric joint models of competing risks survival and skewed longitudinal data using integrated nested Laplace approximation. [PDF]
BMC Med Res MethodolFerede MM, Nakhaei Rad N, Chen DG.
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Differential Privacy for Bayesian Inference through Posterior Sampling
, 2017 Christos Dimitrakakis +4 more
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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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2019
Abstract Chapter 4 introduces the concept of Bayesian inference. The chapter discusses the scientific method, and illustrates how Bayes’ Theorem can be used for scientific inference. Bayesian inference is the use of Bayes’ Theorem to draw conclusions about a set of mutually exclusive and exhaustive alternative hypotheses by linking prior
Therese M. Donovan, Ruth M. Mickey
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Abstract Chapter 4 introduces the concept of Bayesian inference. The chapter discusses the scientific method, and illustrates how Bayes’ Theorem can be used for scientific inference. Bayesian inference is the use of Bayes’ Theorem to draw conclusions about a set of mutually exclusive and exhaustive alternative hypotheses by linking prior
Therese M. Donovan, Ruth M. Mickey
+4 more sources
Bayesian statistics and modelling
Nature Reviews Methods Primers, 2021Rens van de Schoot +2 more
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