Dimension of Marginals of Kronecker Product Models

11/10/2015
by   Guido Montufar, et al.
0

A Kronecker product model is the set of visible marginal probability distributions of an exponential family whose sufficient statistics matrix factorizes as a Kronecker product of two matrices, one for the visible variables and one for the hidden variables. We estimate the dimension of these models by the maximum rank of the Jacobian in the limit of large parameters. The limit is described by the tropical morphism; a piecewise linear map with pieces corresponding to slicings of the visible matrix by the normal fan of the hidden matrix. We obtain combinatorial conditions under which the model has the expected dimension, equal to the minimum of the number of natural parameters and the dimension of the ambient probability simplex. Additionally, we prove that the binary restricted Boltzmann machine always has the expected dimension.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/15/2013

Discrete Restricted Boltzmann Machines

We describe discrete restricted Boltzmann machines: probabilistic graphi...
research
08/14/2015

Hierarchical Models as Marginals of Hierarchical Models

We investigate the representation of hierarchical models in terms of mar...
research
02/13/2013

Asymptotic Model Selection for Directed Networks with Hidden Variables

We extend the Bayesian Information Criterion (BIC), an asymptotic approx...
research
11/05/2012

Kernels and Submodels of Deep Belief Networks

We study the mixtures of factorizing probability distributions represent...
research
11/12/2018

Matrix Product Operator Restricted Boltzmann Machines

A restricted Boltzmann machine (RBM) learns a probability distribution o...
research
02/27/2013

Some Properties of Joint Probability Distributions

Several Artificial Intelligence schemes for reasoning under uncertainty ...
research
04/20/2018

Sampling the Riemann-Theta Boltzmann Machine

We show that the visible sector probability density function of the Riem...

Please sign up or login with your details

Forgot password? Click here to reset