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CHAPTER 7. MCMC
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Default monitors
The default MCMC configuration includes monitors on all top-level stochastic nodes of the
model.
Automated parameter blocking
The default configuration may be replaced by that generated from an automated parameter
blocking algorithm. This algorithm determines groupings of model nodes that, when jointly
sampled with a RW block sampler, increase overall MCMC efficiency. Overall efficiency is
defined as the effective sample size of the slowest-mixing node divided by computation time.
This is done by:
autoBlockSpec <- configureMCMC(Rmodel, autoBlock = TRUE)
In this usage, the additional control argument autoIt may also be provided to indicate the
number of MCMC samples to be used in the automated blocking procedure (default 20,000).
Note that this function compiles and runs MCMCs, progressively exploring different sampler
assignments, so it takes some time and generates some output.
7.1.2
Customizing the MCMC configuration
The MCMC configuration may be customized in a variety of ways, either through additional
named arguments to configureMCMC or by calling member methods of an existing MCMCspec
object.
Default samplers for particular nodes
One can create an MCMC configuration with default samplers on just a particular set of
nodes using the nodes argument to configureMCMC. The value for the nodes argument may
be a character vector containing node and/or variable names. In the case of a variable name,
a default sampler will be added for all stochastic nodes in the variable.
If the nodes argument is provided, default samplers are created only for the stochastic
nodes specified by this argument (possibly including data nodes), and the ordering of these
sampling algorithms matches the ordering within the nodes argument. It is worthwhile to
note this is the only way in which a sampler may be placed on a data node, which upon
execution of the MCMC will overwrite any value stored in the data node.
Creating a configuration with no samplers
If you plan to customize the choice of all samplers, it can be useful to obtain a configuration
with no sampler assignments at all. This can be done by providing the nodes argument with
the value NULL, character(), or list().