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- W2907819039 abstract "The Markov Chain Monte-Carlo (MCMC) born in early 1950s has recently aroused great interestamong statisticians, particularly researchers working in image analysis, discrete optimization, neuralnetworks, genetic sequencing and other related Eelds. Recent theoretical achievements in resamplingprocedures provide a new perspective for handling errors in Bayesian inference, which treats all unknownsas random variables. The unknowns include uncertainties in the model such as fixed effects, randomeffects, unobserved indicator variables and missing data. Only in few cases, the posterior distribution is instandard analytic form. In most other models like generalized linear models, mixture models,epidemiological models and survival analysis, the exact analytic Bayesian inference is impossible. Thispaper surveys some of the recent advances in this area that allows exact Bayesian computation usingsimulations and discusses some applications to biomedical data." @default.
- W2907819039 created "2019-01-11" @default.
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- W2907819039 date "2008-01-01" @default.
- W2907819039 modified "2023-09-26" @default.
- W2907819039 title "Markov chain monte Carlo methods in Bayesian Inference" @default.
- W2907819039 hasPublicationYear "2008" @default.
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