EXAMPLES OF MARKOV CHAIN SAMPLING FOR SIMPLE DISTRIBUTIONS Almost all uses of the software in this package involve sampling from a distribution using Markov chain methods, and then making Monte Carlo estimates for the expectations of functions of state based on this sample. This is done, for example, when make predictions for test cases based on the posterior distribution of a neural network model. Some ways of doing Markov chain sampling are illustrated in the examples of modeling with neural networks, Gaussian processes, etc. If your main interest is in those models, you could start with those examples, but the simpler examples in this section may be more helpful in understanding the Markov chain methods. These examples also introduce the facilities of the 'dist' module, which are used when sampling from Bayesian models defined using formulas for the prior and likelihood, as illustrated by the examples in Ex-bayes.doc. The examples there also illustrate some additional aspects of Markov chain sampling. The commands used in these examples can also be found in command files in the "ex-dist" directory.