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What is expectation propagation algorithm?
From Wikipedia, the free encyclopedia. Expectation propagation (EP) is a technique in Bayesian machine learning. EP finds approximations to a probability distribution. It uses an iterative approach that leverages the factorization structure of the target distribution.
Why feature selection is important in machine learning?
Top reasons to use feature selection are: It enables the machine learning algorithm to train faster. It reduces the complexity of a model and makes it easier to interpret. It improves the accuracy of a model if the right subset is chosen.
What is assumed density filtering?
Assumed Density Filtering (ADF) is a general technique for approximating the true posterior with a tractable paramet- ric distribution in Bayesian networks. Thus, for online Bayesian filtering, the parameters for the ADF estimate is given by θt+1 = argminθ KL(p(·|θt,Dt)||p(·|θ)).
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.
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.