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Expectation Propagation (EP)
An approximate inference technique used in probabilistic models, particularly in machine learning, for efficiently estimating complex distributions.
Implications
A probabilistic inference technique used in machine learning and statistics, where complex distributions are approximated by simpler ones, enabling efficient calculation of expectations in large, complex models.
Example
Example: A data scientist uses expectation propagation to approximate the posterior distributions in a complex Bayesian network, allowing for faster and more scalable inference.
Related Terms
Different from the Expectation-Maximization (EM) algorithm, which finds maximum likelihood estimates, EP focuses on efficiently calculating approximate expectations in probabilistic models.
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