<description>The Online Expectation-Maximization (EM) is a generic algorithm that can be used to estimate the parameters of latent data models incrementally from large volumes of data. The general principle of the approach is to use a stochastic approximation scheme, in the domain of sufficient statistics, as a proxy for a limiting, deterministic, population version of the EM recursion. In this talk, I will briefly review the convergence properties of the method and discuss some applications and extensions of the basic approach. </description>

StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"

Statlearn2013

Learning with the Online EM Algorithm (Olivier Cappé)

MAY 16, 201366 MIN
StatLearn 2013 - Workshop on "Challenging problems in Statistical Learning"

Learning with the Online EM Algorithm (Olivier Cappé)

MAY 16, 201366 MIN

Description

The Online Expectation-Maximization (EM) is a generic algorithm that can be used to estimate the parameters of latent data models incrementally from large volumes of data. The general principle of the approach is to use a stochastic approximation scheme, in the domain of sufficient statistics, as a proxy for a limiting, deterministic, population version of the EM recursion. In this talk, I will briefly review the convergence properties of the method and discuss some applications and extensions of the basic approach.