Results 251 to 260 of about 149,758 (276)

Approximate Bayesian inference for random effects meta‐analysis

Statistics in Medicine, 1998
Whilst meta-analysis is becoming a more commonplace statistical technique, Bayesian inference in meta-analysis requires complex computational techniques to be routinely applied. We consider simple approximations for the first and second moments of the parameters of a Bayesian random effects model for meta-analysis.
K, Abrams, B, Sansó
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The variational approximation for Bayesian inference

IEEE Signal Processing Magazine, 2008
The influence of this Thomas Bayes' work was immense. It was from here that "Bayesian" ideas first spread through the mathematical world, as Bayes's own article was ignored until 1780 and played no important role in scientific debate until the 20th century.
Dimitris G. Tzikas, Aristidis C. Likas, Nikolaos P. Galatsanos   +2 more
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Approximate Bayesian inference under informative sampling

Biometrika, 2017
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Z., Kim, J. K., Yang, S.
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A Bayesian Approach to Approximate Conditional Inference

Biometrika, 1995
Summary: The author [ibid., 1-23 (1995; see the preceding entry Zbl 0829.62003)] studies regular Bayesian and frequentist approximations within a unified framework in the case of a single parameter, and shows that higher-order approximations to sampling distributions arise from their Bayesian counterparts via an unsmoothing argument.
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Approximate Bayesian inference for simple mixtures

2000
Exact likelihoods and posterior densities associated with mixture data are computationally complex because of the large number of terms involved, corresponding to the large number of possible ways in which the observations might have evolved from the different components of the mixture.
K. Humphreys, D. M. Titterington
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Generalized Laplacian approximations in Bayesian inference

Canadian Journal of Statistics, 1995
AbstractThis paper presents a new Laplacian approximation to the posterior density of η = g(θ). It has a simpler analytical form than that described by Leonard et al. (1989). The approximation derived by Leonard et al. requires a conditional information matrix Rη to be positive definite for every fixed η. However, in many cases, not all Rη are positive
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Approximate Bayesian Inference

2022
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Approximate Bayesian Inference for Latent Gaussian models by using Integrated Nested Laplace Approximations

Journal of the Royal Statistical Society Series B: Statistical Methodology, 2009
Håvard Rue, Sara Martino, Nicolas Chopin   +2 more
exaly  

Efficient approximate inference in Bayesian networks with continuous variables

Reliability Engineering and System Safety, 2018
Chenzhao Li, Sankaran Mahadevan
exaly  

A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation

Mathematical Biosciences, 2017
Theodore Kypraios, Peter Neal, Dennis Prangle   +2 more
exaly