A mirror descent approach to maximum likelihood estimation in latent variable models

Fuente: arXiv
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Autor principal: Crucinio, Francesca R.
Formato: Preprint
Publicado: 2025
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author Crucinio, Francesca R.
author_facet Crucinio, Francesca R.
contents We introduce an approach based on mirror descent and sequential Monte Carlo (SMC) to perform joint parameter inference and posterior estimation in latent variable models. This approach is based on minimisation of a functional over the parameter space and the space of probability distributions and, contrary to other popular approaches, can be implemented when the latent variable takes values in discrete spaces. We provide a detailed theoretical analysis of both the mirror descent algorithm and its approximation via SMC. We experimentally show that the proposed algorithm outperforms standard expectation maximisation algorithms and is competitive with other popular methods for real-valued latent variables.
format Preprint
id arxiv_https___arxiv_org_abs_2501_15896
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A mirror descent approach to maximum likelihood estimation in latent variable models
Crucinio, Francesca R.
Computation
Methodology
We introduce an approach based on mirror descent and sequential Monte Carlo (SMC) to perform joint parameter inference and posterior estimation in latent variable models. This approach is based on minimisation of a functional over the parameter space and the space of probability distributions and, contrary to other popular approaches, can be implemented when the latent variable takes values in discrete spaces. We provide a detailed theoretical analysis of both the mirror descent algorithm and its approximation via SMC. We experimentally show that the proposed algorithm outperforms standard expectation maximisation algorithms and is competitive with other popular methods for real-valued latent variables.
title A mirror descent approach to maximum likelihood estimation in latent variable models
topic Computation
Methodology
url https://arxiv.org/abs/2501.15896