[BOOK][B] Markov chain Monte Carlo: stochastic simulation for Bayesian inference
D Gamerman, HF Lopes - 2006 - taylorfrancis.com
While there have been few theoretical contributions on the Markov Chain Monte Carlo
(MCMC) methods in the past decade, current understanding and application of MCMC to the …
(MCMC) methods in the past decade, current understanding and application of MCMC to the …
[BOOK][B] Dynamic bayesian networks: representation, inference and learning
KP Murphy - 2002 - search.proquest.com
Modelling sequential data is important in many areas of science and engineering. Hidden
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …
Markov models (HMMs) and Kalman filter models (KFMs) are popular for this because they …
[BOOK][B] Bayesian modeling using WinBUGS
I Ntzoufras - 2011 - books.google.com
A hands-on introduction to the principles of Bayesian modeling using WinBUGS Bayesian
Modeling Using WinBUGS provides an easily accessible introduction to the use of …
Modeling Using WinBUGS provides an easily accessible introduction to the use of …
Bayesian inference for categorical data analysis
This article surveys Bayesian methods for categorical data analysis, with primary emphasis
on contingency table analysis. Early innovations were proposed by Good (1953, 1956 …
on contingency table analysis. Early innovations were proposed by Good (1953, 1956 …
The pseudo-marginal approach for efficient Monte Carlo computations
C Andrieu, GO Roberts - 2009 - projecteuclid.org
We introduce a powerful and flexible MCMC algorithm for stochastic simulation. The method
builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139 …
builds on a pseudo-marginal method originally introduced in [Genetics 164 (2003) 1139 …
[BOOK][B] Computational statistics
GH Givens, JA Hoeting - 2012 - books.google.com
This new edition continues to serve as a comprehensive guide to modern and classical
methods of statistical computing. The book is comprised of four main parts spanning the …
methods of statistical computing. The book is comprised of four main parts spanning the …
The practical implementation of Bayesian model selection
In principle, the Bayesian approach to model selection is straightforward. Prior probability
distributions are used to describe the uncertainty surrounding all unknowns. After observing …
distributions are used to describe the uncertainty surrounding all unknowns. After observing …
Model uncertainty
The evolution of Bayesian approaches for model uncertainty over the past decade has been
remarkable. Catalyzed by advances in methods and technology for posterior computation …
remarkable. Catalyzed by advances in methods and technology for posterior computation …
On Bayesian model and variable selection using MCMC
Several MCMC methods have been proposed for estimating probabilities of models and
associated'model-averaged'posterior distributions in the presence of model uncertainty. We …
associated'model-averaged'posterior distributions in the presence of model uncertainty. We …
Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions
The major implementational problem for reversible jump Markov chain Monte Carlo methods
is that there is commonly no natural way to choose jump proposals since there is no …
is that there is commonly no natural way to choose jump proposals since there is no …