Spike Train Analysis Toolkit Documentation  Spike Train Analysis Toolkit Function Reference  ctwmcmcsample

Spike Train Analysis Toolkit

ctwmcmcsample

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.

Cross-reference information

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