# Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo

arXiv: https://arxiv.org/abs/1311.4117 · HTML: https://mlsanity.com/paper/1311.4117/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2013.

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

- [Likelihood-Free Inference in State-Space Models with Unknown Dynamics](https://mlsanity.com/paper/2111.01555/) (2021)
- [Dimension-reduced Reconstruction Map Learning for Parameter Estimation in Likelihood-Free Inference Problems](https://mlsanity.com/paper/2407.13971/) (2024)

## Abstract (arXiv)

We propose sequential Monte Carlo based algorithms for maximum likelihood estimation of the static parameters in hidden Markov models with an intractable likelihood using ideas from approximate Bayesian computation. The static parameter estimation algorithms are gradient based and cover both offline and online estimation. We demonstrate their performance by estimating the parameters of three intractable models, namely the alpha-stable distribution, g-and-k distribution, and the stochastic volatility model with alpha-stable returns, using both real and synthetic data.