CTWMCMCSAMPLE Perform MCMC sampling of entropy on CTW tree graph(s).
Y = CTWMCMCSAMPLE(X,OPTS) returns both the analytic (or weighted)
entropy estimation on the input context-tree weighted (CTW) graph(s),
and numerous Markov chain Monte Carlo (MCMC) Bayesian samples derived
from the CTW tree graph(s). The type of analytic entropy calculated
depends on the options, as does the number of MCMC sample to return.
The input X is a representation of CTW tree graph(s), as either a cell
array or structure, as obtained from CTWMCMCTREE. The output Y is a
structure containing both analytic and MCMC entropy estimates.
The options and parameters for this function are:
OPTS.nmc: The number of MCMC samples to make. Its value must be
greater than 0, and should be at least 100. The default is 199.
OPTS.entropy_estimation_method: A cell array of entropy estimation
methods. Please see the Spike Train Analysis Toolkit
documentation for more information, and corresponding entropy
options. The default is {'plugin'}.
OPTS.variance_estimation_method: A cell array of variance
estimation methods. Please see the Spike Train Analysis Toolkit
documentation for more information, and corresponding variance
options (listed with entropy options). The default is not to
perform any variance estimation.
OPTS.memory_expansion: The ratio by which tree memory is expanded
when reallocation become necessary during tree building. Its
value must be greater than or equal to 1. The default is 1.61.
OPTS.mcmc_iterations: The absolute number of iterations to run the
Markov chain Monte Carlo simulation (for each OPTS.nmc sample).
If OPTS.mcmc_min_acceptances probability vectors have been
accepted, this is also the minimum number of iterations. The
default is 100.
OPTS.mcmc_max_iterations: The maximum number of Markov chain Monte
Carlo iterations. The simulation runs OPTS.mcmc_iterations sized
batches of iterations until OPTS.mcmc_min_acceptances probability
vectors are accepted, or this number is reached. The default is
10000.
OPTS.mcmc_min_acceptances: The minimum number of Markov chain Monte
Carlo acceptances, that is the number of acceptable probability
vectors. The default is 20.
Y = CTWMCMCSAMPLE(X) uses the default options and parameters.
[Y,OPTS_USED] = CTWMCMCSAMPLE(X) or [Y,OPTS_USED] =
CTWMCMCSAMPLE(X,OPTS) additionally return the options used.
See also DIRECTBIN, CTWMCMC, CTWMCMCTREE, CTWMCMCINFO.
This function calls: