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Article{osti_6538, title = {markov transitions and the propagation of chaos}, author = {gottlieb, alexander david}, abstractnote = {the propagation of Chaos is a middle concept of energizing theory that serves to relate the equations of Ludwig Boltzmann and vlasov to the dynamics of many-particle systems.
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This method acting, ``expectation propagation,'' unifies and generalizes cardinal previous techniques: assumed-density filtering, an wing of the kalman filter, and kooky belief propagation, AN extension of belief.
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Our contributions include new-sprung theoretical models, applied math methods, and rendition algorithms.
How is expectation propagation related to belief states?
Expectation Propagation approximates the belief states with expectations, such as means and variances, giving it much wider scope. Expectation Propagation also extends belief propagation in the opposite direction---propagating richer belief states which incorporate correlations between variables.
How is expectation propagation used in pattern recognition?
For pattern recognition, Expectation Propagation provides an algorithm for training Bayes Point Machine classifiers that is faster and more accurate than any previously known. The resulting classifiers outperform Support Vector Machines on several standard datasets, in addition to having a comparable training time.
Which is an extension of the expectation propagation method?
This method, ``Expectation Propagation,'' unifies and generalizes two previous techniques: assumed-density filtering, an extension of the Kalman filter, and loopy belief propagation, an extension of belief propagation in Bayesian networks.
Why is expectation propagation useful for discrete networks?
Loopy belief propagation, because it propagates exact belief states, is useful for limited types of belief networks, such as purely discrete networks. Expectation Propagation approximates the belief states with expectations, such as means and variances, giving it much wider scope.
Last Update: Oct 2021
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19.10.2021 08:22
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