# Unbiased Parameter Inference for a Class of Partially Observed Levy-Process Models

arXiv: https://arxiv.org/abs/2112.13874 · HTML: https://mlsanity.com/paper/2112.13874/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2021.

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

- [Unbiased Estimation using Underdamped Langevin Dynamics](https://mlsanity.com/paper/2206.07202/) (2022)

## Abstract (arXiv)

We consider the problem of static Bayesian inference for partially observed Levy-process models. We develop a methodology which allows one to infer static parameters and some states of the process, without a bias from the time-discretization of the afore-mentioned Levy process. The unbiased method is exceptionally amenable to parallel implementation and can be computationally efficient relative to competing approaches. We implement the method on S & P 500 log-return daily data and compare it to some Markov chain Monte Carlo (MCMC) algorithm.