We develop a mean eld theory for sigmoid belief networks based on ideas from statistical mechanics. Our mean eld theory provides a tractable approximation to the true probability dis-tribution in these networks; it also yields a lower bound on the likelihood of evidence. We demon-strate the utility of this framework on a benchmark problem in statistical pattern recognition|the classi cation of handwritten digits. 1
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting witho...
Bayesian belief networks can represent the complicated probabilistic processes that form natural sen...
More and more real-life applications of the belief network framework begin to emerge. As application...
We develop a mean eld theory for sigmoid belief networks based on ideas from statistical mechanics. ...
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics...
The chief aim of this paper is to propose mean-eld approximations for a broad class of Belief networ...
The chief aim of this paper is to propose mean-field approximations for a broad class of Belief ne...
Exact inference in large, densely connected probabilistic networks is computa-tionally intractable, ...
Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework ...
The mean field algorithm is a widely used approximate inference algorithm for graphical models whose...
The mean field algorithm is a widely used approximate inference algorithm for graphical models whose...
Exact inference in densely connected Bayesian networks is computationally intractable, and so there ...
Exact inference in densely connected Bayesian networks is computation-ally intractable, and so there...
In this paper, we derive a second order mean field theory for directed graphical probability models....
Mean-field analysis is an important tool for understanding dynamics on complex networks. However, su...
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting witho...
Bayesian belief networks can represent the complicated probabilistic processes that form natural sen...
More and more real-life applications of the belief network framework begin to emerge. As application...
We develop a mean eld theory for sigmoid belief networks based on ideas from statistical mechanics. ...
We develop a mean field theory for sigmoid belief networks based on ideas from statistical mechanics...
The chief aim of this paper is to propose mean-eld approximations for a broad class of Belief networ...
The chief aim of this paper is to propose mean-field approximations for a broad class of Belief ne...
Exact inference in large, densely connected probabilistic networks is computa-tionally intractable, ...
Sigmoid type belief networks, a class of probabilistic neural networks, provide a natural framework ...
The mean field algorithm is a widely used approximate inference algorithm for graphical models whose...
The mean field algorithm is a widely used approximate inference algorithm for graphical models whose...
Exact inference in densely connected Bayesian networks is computationally intractable, and so there ...
Exact inference in densely connected Bayesian networks is computation-ally intractable, and so there...
In this paper, we derive a second order mean field theory for directed graphical probability models....
Mean-field analysis is an important tool for understanding dynamics on complex networks. However, su...
We present a systematic approach to mean field theory (MFT) in a general probabilistic setting witho...
Bayesian belief networks can represent the complicated probabilistic processes that form natural sen...
More and more real-life applications of the belief network framework begin to emerge. As application...
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