Momentum Particle Maximum Likelihood

Maximum likelihood estimation (MLE) of latent variable models is often recast as an optimization problem over the extended space of parameters and probability distributions. For example, the Expectation Maximization (EM) algorithm can be interpreted as coordinate descent applied to a suitable free energy functional over this space. Recently, this perspective has been combined with insights from optimal transport and Wasserstein gradient flows to develop particle-based algorithms applicable to wider classes of models than standard EM.
Drawing inspiration from prior works …

algorithm cs.lg energy example free functional insights interpreted likelihood maximum likelihood estimation mle optimization parameters perspective probability space