Particle algorithms for maximum likelihood training of latent variable models

Juan Kuntz; Jen Ning Lim; Adam M. Johansen

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arXiv · 2204.12965

Particle algorithms for maximum likelihood training of latent variable models

Juan Kuntz·Jen Ning Lim·Adam M. Johansen

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Abstract

(Neal and Hinton, 1998) recast maximum likelihood estimation of any given latent variable model as the minimization of a free energy functional $F$, and the EM algorithm as coordinate descent applied to $F$. Here, we explore alternative ways to optimize the functional. In particular, we identify various gradient flows associated with $F$ and show that their limits coincide with $F$'s stationary points. By discretizing the flows, we obtain practical particle-based algorithms for maximum likelihood estimation in broad classes of latent variable models. The novel algorithms scale to high-dimensional settings and perform well in numerical experiments.

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BibTeX RIS

Juan Kuntz, Jen Ning Lim, Adam M. Johansen. 2022-04-27. Particle algorithms for maximum likelihood training of latent variable models. https://arxiv.org/abs/2204.12965

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