This book provides an introduction to the Bayesian approach to
statistical analysis of data, written at a level that is accessible to a
social science audience. The book covers the Bayesian approach from
model development through the development and implementation of programs
to estimate the model, through summation and interpretation of the
output. The first part provides a detailed introduction to mathematical
statistics and the Bayesian approach to statistics, as well as a
thorough explanation of the rationale for using simulation methods to
construct summaries of posterior distributions. Markov chain Monte Carlo
(MCMC) methods--including the Gibbs sampler and the Metropolis-Hastings
algorithm--are then introduced as general methods for simulating samples
from distributions. Extensive discussion of programming Markov chain
Monte Carlo algorithms, monitoring their performance, and improving them
is provided before turning to the larger examples involving real social
science models and data.