fitfvbm: Maximum pseudolikelihood estimation of a fully-visible...

In BoltzMM: Boltzmann Machines with MM Algorithms

Maximum pseudolikelihood estimation of a fully-visible Boltzmann machine.

Description

Estimates the bias vector and interaction matrix of a fully-visible Boltzmann machine via maximum pseudolikelihood estimation using an MM algorithm.

Usage

fitfvbm(data, bvec, Mmat, delta_crit = 0.001, max_it = 1000L)

Arguments

data	An N by n matrix, where each of the N rows contains a length n string of spin variables (i.e. each element is -1 or 1).
bvec	Initial estimate for a vector of length n containing real valued bias parameters.
Mmat	Initial estimate for a symmetric n by n matrix, with zeros along the diagonal, containing the interaction parameters.
delta_crit	Real threshold value for the convergence criterion, based on the relative change in the Euclidean distance of parameter estimates from consecutive iterations.
max_it	Integer value indicating the maximum number of iterations that the algorithm is to run for.

Value

A list containing 4 objects: the final log-pseudolikelihood value pll, a vector containing the estimate of the bias parameters bvec, a matrix containing the estimate of the interaction parameters Mmat, and the number of algorithm iterations itt.

Author(s)

Andrew T. Jones and Hien D. Nguyen

References

H.D. Nguyen and I.A. Wood (2016), A block successive lower-bound maximization algorithm for the maximum pseudolikelihood estimation of fully visible Boltzmann machines, Neural Computation, vol 28, pp. 485-492

Examples

# Generate num=1000 random strings of n=3 binary spin variables under bvec and Mmat.
num <- 1000
bvec <- c(0,0.5,0.25)
Mmat <- matrix(0.1,3,3) - diag(0.1,3,3)
data <- rfvbm(num,bvec,Mmat)
# Fit a fully visible Boltzmann machine to data, starting from parameters bvec and Mmat.
fitfvbm(data,bvec,Mmat)

BoltzMM documentation built on April 13, 2025, 5:09 p.m.