Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov
chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed
in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without
the need to write a MCMC sampler. The ...
Bayesian inference using Gibbs sampling (BUGS) is a set of statistical software that uses Markov
chain Monte Carlo (MCMC) methods to estimate almost any specified model. Originally developed
in the late 1980s, the software is an excellent introduction to applied Bayesian statistics without
the need to write a MCMC sampler. The software is typically used for regression-based analyses,
but any models that can be specified using graphical nodes are possible. Advanced topics such as missing data, spatial analysis, model comparison and dynamic infectious disease models can be
tackled. Three examples are provided; a linear regression model to illustrate parameter estimation,
the steps to ensure that the estimates have converged and a comparison of run-times across different
computing platforms. The second example describes a model that estimates the probability of being
vaccinated from cross-sectional and surveillance data, and illustrates the specification of different
models, model comparison and data augmentation. The third example illustrates estimation of
parameters within a dynamic Susceptible-Infected-Recovered model. These examples show that
BUGS can be used to estimate parameters from models relevant for infectious diseases, and provide
an overview of the relative merits of the approach taken.