# Markov Observation Models

arXiv: https://arxiv.org/abs/2208.06368 · HTML: https://mlsanity.com/paper/2208.06368/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 41 references that are in the map.
- First posted on arXiv 12 Aug 2022.

## Builds on (most cited first, 1 of 1)

- [Modelling multi-scale state-switching functional data with hidden Markov models](https://mlsanity.com/paper/2101.03268/) (2021)

## Abstract (arXiv)

Herein, the Hidden Markov Model is expanded to allow for Markov chain observations. In particular, the observations are assumed to be a Markov chain whose one step transition probabilities depend upon the hidden Markov chain. An Expectation-Maximization analog to the Baum-Welch algorithm is developed for this more general model to estimate the transition probabilities for both the hidden state and for the observations as well as to estimate the probabilities for the initial joint hidden-state-observation distribution. A believe state or filter recursion to track the hidden state then arises from the calculations of this Expectation-Maximization algorithm. A dynamic programming analog to the Viterbi algorithm is also developed to estimate the most likely sequence of hidden states given the sequence of observations.