# An adaptive simulated annealing EM algorithm for inference on non-homogeneous hidden Markov models

arXiv: https://arxiv.org/abs/1912.09733 · HTML: https://mlsanity.com/paper/1912.09733/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis](https://mlsanity.com/paper/2207.09765/) (2022)

## Abstract (arXiv)

Non-homogeneous hidden Markov models (NHHMM) are a subclass of dependent mixture models used for semi-supervised learning, where both transition probabilities between the latent states and mean parameter of the probability distribution of the responses (for a given state) depend on the set of p covariates. A priori we do not know which (and how) covariates influence the transition probabilities and the mean parameters. This induces a complex combinatorial optimization problem for model selection with 4p potential configurations. To address the problem, in this article we propose an adaptive (A) simulated annealing (SA) expectation maximization (EM) algorithm (ASA-EM) for joint optimization of models and their parameters with respect to a criterion of interest.