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Universal Approximation Depth and Errors of Narrow Belief Networks with Discrete Units
Deep belief network restricted Boltzmann machine universal approxima-tion representational power Kullback-Leibler divergence,q-ary variable
2013/4/28
We generalize recent theoretical work on the minimal number of layers of narrow deep belief networks that can approximate any probability distribution on the states of their visible units arbitrarily ...
Dependent Dirichlet Priors and Optimal Linear Estimators for Belief Net Parameters
Dependent Dirichlet Priors Optimal Linear Estimators Belief Net Parameters
2012/9/19
A Bayesian belief network is a model of a joint distribution over a finite set of vari-ables, with a DAG structure representing im-mediate dependencies among the variables.For each node, a table of pa...
Local stability of Belief Propagation algorithm with multiple fixed points
Local stability Belief Propagation algorithm multiple fixed points
2012/9/19
A number of problems in statistical physics and computer science can be expressed as the computation of marginal probabilities over a Markov random field. Belief propagation, an iterative message-pass...
A betting interpretation for probabilities and Dempster-Shafer degrees of belief
betting interpretation probabilities Dempster-Shafer degrees belief
2010/3/9
There are at least two ways to interpret numerical degrees of belief in terms of
betting:
1. You can offer to bet at the odds defined by the degrees of belief.
2. You can make the judgement that a ...
Exact distribution theory for belief net responses
Bayesian network BDeprior belief network beta distribution query response
2009/9/22
Bayesian belief networks provide estimates of conditional probabilities,
called query responses. The precision of these estimates is assessed using posterior
distributions. This paper discusses two ...
Fixing Convergence of Gaussian Belief Propagation
Fixing Convergence Gaussian Belief Propagation
2010/3/17
Gaussian belief propagation (GaBP) is an iterative
message-passing algorithm for inference in Gaussian graphical
models. It is known that when GaBP converges it converges to the
correct MAP estimat...