# Autoregressive Asymmetric Linear Gaussian Hidden Markov Models

arXiv: https://arxiv.org/abs/2010.15604 · HTML: https://mlsanity.com/paper/2010.15604/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2020.

## Built on by (most cited first, 2 of 2)

- [Quantum Approximate Optimization Algorithm for Bayesian network structure learning](https://mlsanity.com/paper/2203.02400/) (2022)
- [Context-specific kernel-based hidden Markov model for time series analysis](https://mlsanity.com/paper/2301.09870/) (2023)

## Abstract (arXiv)

In a real life process evolving over time, the relationship between its relevant variables may change. Therefore, it is advantageous to have different inference models for each state of the process. Asymmetric hidden Markov models fulfil this dynamical requirement and provide a framework where the trend of the process can be expressed as a latent variable. In this paper, we modify these recent asymmetric hidden Markov models to have an asymmetric autoregressive component, allowing the model to choose the order of autoregression that maximizes its penalized likelihood for a given training set. Additionally, we show how inference, hidden states decoding and parameter learning must be adapted to fit the proposed model. Finally, we run experiments with synthetic and real data to show the capabilities of this new model.