Fitting genetic models using Markov Chain Monte Carlo algorithms with BUGS.

Spatial count models on the number of unhealthy days in Tehran

Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on poisson (poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models i...

MCMC for Generalized Linear Mixed Models with glmmBUGS

The glmmBUGS package is a bridging tool between Generalized Linear Mixed Models (GLMMs) in R and the BUGS language. It provides a simple way of performing Bayesian inference using Markov Chain Monte Carlo (MCMC) methods, taking a model formula and data frame in R and writing a BUGS model file, data file, and initial values files. Functions are provided to reformat and summarize the BUGS results...

Bayesian Inference and Decision Theory – A Coherent Framework for Decision Making in Natural Resource Management

Bayesian inference and decision theory may be used in the solution of relatively complex problems of natural resource management, owing to recent advances in statistical theory and computing. In particular, Markov chain Monte Carlo algorithms provide a computational framework for fitting models of adequate complexity and for evaluating the expected consequences of alternative management actions...

Norges Teknisk-naturvitenskapelige Universitet Fitting Gaussian Markov Random Fields to Gaussian Fields Fitting Gaussian Markov Random Fields to Gaussian Fields Tmr Project on Spatial Statistics (erb-fmrx-ct960095) for Support and Inspiration

SUMMARY This paper discusses the following task often encountered building Bayesian spatial models: construct a homogeneous Gaussian Markov random field (GMRF) on a lattice with correlation properties either as present in observed data or consistent with prior knowledge. The Markov property is essential in design of computational efficient Markov chain Monte Carlo algorithms used to analyse suc...

MCMC algorithms for fitting Bayesian models