We present techniques for computing upper and lower bounds on the likelihoods of partial instantiations of variables in sigmoid and noisy-OR networks. The bounds determine confidence intervals for the desired likelihoods and become useful when the size of the network (or clique size) precludes exact computations. We illustrate the tightness of the obtained bounds by numerical experiments
Bayesian networks provide a useful mechanism for encoding and reasoning about uncertainty. Recent pr...
<p>Models with intractable likelihood functions arise in areas including network analysis and spatia...
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...
Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framewor...
We study two-layer belief networks of binary random variables in which the conditional probabilities...
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechani...
This dissertation discusses several aspects of estimation and inference for high dimensional network...
Today witnesses an explosion of data coming from various types of networks such as online social net...
We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic...
Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence nod...
Contains fulltext : 182072.pdf (publisher's version ) (Closed access)Computing pos...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
We calculate lower bounds on the size of sigmoidal neural networks that approximate continuous funct...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
Contains fulltext : 160422.pdf (publisher's version ) (Open Access)Computing poste...
Bayesian networks provide a useful mechanism for encoding and reasoning about uncertainty. Recent pr...
<p>Models with intractable likelihood functions arise in areas including network analysis and spatia...
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...
Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framewor...
We study two-layer belief networks of binary random variables in which the conditional probabilities...
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechani...
This dissertation discusses several aspects of estimation and inference for high dimensional network...
Today witnesses an explosion of data coming from various types of networks such as online social net...
We study probabilistic inference in large, layered Bayesian networks represented as directed acyclic...
Approximating the inference probability Pr[X = xjE = e] in any sense, even for a single evidence nod...
Contains fulltext : 182072.pdf (publisher's version ) (Closed access)Computing pos...
AbstractApproximating the inference probability Pr[X = x | E = e] in any sense, even for a single ev...
We calculate lower bounds on the size of sigmoidal neural networks that approximate continuous funct...
Computation of marginal probabilities in Bayesian Belief Networks is central to many probabilistic r...
Contains fulltext : 160422.pdf (publisher's version ) (Open Access)Computing poste...
Bayesian networks provide a useful mechanism for encoding and reasoning about uncertainty. Recent pr...
<p>Models with intractable likelihood functions arise in areas including network analysis and spatia...
Probabilistic graphical models have been successfully applied to a wide variety of fields such as co...