    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.