PMC:4307189 / 19013-19947 JSONTXT

{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4307189","sourcedb":"PMC","sourceid":"4307189","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4307189","text":"Computing the exact solution for the marginal likelihood is often intractable since it is prone to the curse of dimensionality. Fortunately, Markov Chain Monte Carlo (MCMC) methods such as Gibbs sampling and Metropolis-Hastings methods do not require to be explicitly computed. In general, MCMC methods are stochastic simulation techniques which generate samples from the joint distribution Pℳ,θ|D for calculating the posterior probabilities of parameters. Here we used Gibbs sampling methods, which sample iteratively, one parameter at a time, from the full conditional distribution given the current and previous values of all other parameters. To implement Gibbs sampling, we employed WinBUGS [29], which is a high-level software package providing an easy interface for implementing complex Bayesian models. In WinBUGS, users are free from background lower-level programming details, and only have to express the model precisely.","tracks":[]}