From 8070dc963753142bb86c4ed698d91fd623ed28e7 Mon Sep 17 00:00:00 2001 From: ziejd2 Date: Thu, 28 Sep 2017 15:04:40 -0500 Subject: BNW using Octave instead of Matlab. This version of BNW should perform the same as the original version. The only difference is that it uses Octave instead of Matlab when running BayesNet Toolbox during parameter learning. I am calling this BNW_1.02. It can be accessed at: compbio.uthsc.edu/BNW_1.02 --- sourcecodes/bnt-master/nethelp3.3/metrop.htm | 108 +++++++++++++++++++++++++++ 1 file changed, 108 insertions(+) create mode 100644 sourcecodes/bnt-master/nethelp3.3/metrop.htm (limited to 'sourcecodes/bnt-master/nethelp3.3/metrop.htm') diff --git a/sourcecodes/bnt-master/nethelp3.3/metrop.htm b/sourcecodes/bnt-master/nethelp3.3/metrop.htm new file mode 100644 index 00000000..93c3ed49 --- /dev/null +++ b/sourcecodes/bnt-master/nethelp3.3/metrop.htm @@ -0,0 +1,108 @@ + + + +Netlab Reference Manual metrop + + + +

metrop +

+Purpose +

+Markov Chain Monte Carlo sampling with Metropolis algorithm. + +

+Synopsis +

+
+samples = metrop(f, x, options)
+samples = metrop(f, x, options, [], P1, P2, ...)
+[samples, energies, diagn] = metrop(f, x, options)
+s = metrop('state')
+metrop('state', s)
+

+ + +

+Description +

+ +samples = metrop(f, x, options) uses +the Metropolis algorithm to sample from the distribution +p ~ exp(-f), where f is the first argument to metrop. +The Markov chain starts at the point x and each +candidate state is picked from a Gaussian proposal distribution and +accepted or rejected according to the Metropolis criterion. + +

samples = metrop(f, x, options, [], p1, p2, ...) allows +additional arguments to be passed to f(). The fourth argument is +ignored, but is included for compatibility with hmc and the +optimisers. + +

[samples, energies, diagn] = metrop(f, x, options) also returns +a log of the energy values (i.e. negative log probabilities) for the +samples in energies and diagn, a structure containing +diagnostic information (position and +acceptance threshold) for each step of the chain in diagn.pos and +diagn.acc respectively. All candidate states (including rejected +ones) are stored in diagn.pos. + +

s = metrop('state') returns a state structure that contains the +state of the two random number generators rand and randn. +These are contained in fields +randstate, +randnstate. + +

metrop('state', s) resets the state to s. If s is an integer, +then it is passed to rand and randn. +If s is a structure returned by metrop('state') then +it resets the generator to exactly the same state. + +

The optional parameters in the options vector have the following +interpretations. + +

options(1) is set to 1 to display the energy values and rejection +threshold at each step of the Markov chain. If the value is 2, then the +position vectors at each step are also displayed. + +

options(14) is the number of samples retained from the Markov chain; +default 100. + +

options(15) is the number of samples omitted from the start of the +chain; default 0. + +

options(18) is the variance of the proposal distribution; default 1. + +

+Examples +

+The following code fragment samples from the posterior distribution of +weights for a neural network. +

+
+w = mlppak(net);
+[samples, energies] = metrop('neterr', w, options, 'netgrad', net, x, t);
+

+ + +

+Algorithm +

+ +The algorithm follows the procedure outlined in Radford Neal's technical +report CRG-TR-93-1 from the University of Toronto. + +

+See Also +

+hmc

+Pages: +Index +