Software for Bayesian inference through Markov chain Monte Carlo in directed statistical models. Its model language uses
dnorm(mean, precision), so the second argument is inverse variance; dcat indexes a supplied probability vector, and dbern introduces a binary latent variable. The model is declarative, so the order of deterministic node definitions does not impose procedural evaluation order. Articles by others on the same topic
WinBUGS (Bayesian Inference Using Gibbs Sampling) is a software package designed for the analysis of Bayesian models using Markov Chain Monte Carlo (MCMC) methods. It allows users to specify a wide range of statistical models in a flexible manner and then perform inference using Bayesian techniques. Key features of WinBUGS include: 1. **Model Specification**: Users can define complex statistical models using a straightforward programming language specifically designed for Bayesian analysis.