In this paper we consider Bayesian parameter inference for partially observed fractional Brownian motion models. The approach we follow is to time-discretize the hidden process and then to design Markov chain Monte Carlo (MCMC) algorithms to sample from the posterior density on the parameters given data. We rely on a novel representation of the time discretization, which seeks to sample from an approximation of the posterior and then corrects via importance sampling; the approximation reduces the time (in terms of total observation time T) by O(T). This method is extended by using a multilevel MCMC method which can reduce the computational cost to achieve a given mean square error versus using a single time discretization. Our methods are illustrated on simulated and real data
Bibliographical noteKAUST Repository Item: Exported on 2023-01-09
Acknowledgements: The authors were supported by KAUST baseline funding.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science
- Statistics and Probability
- Statistics, Probability and Uncertainty