Electronic Journal of Statistics
- Electron. J. Statist.
- Volume 11, Number 2 (2017), 4629-4648.
Geometric ergodicity of Rao and Teh’s algorithm for Markov jump processes and CTBNs
Rao and Teh (2012, 2013) introduced an efficient MCMC algorithm for sampling from the posterior distribution of a hidden Markov jump process. The algorithm is based on the idea of sampling virtual jumps. In the present paper we show that the Markov chain generated by Rao and Teh’s algorithm is geometrically ergodic. To this end we establish a geometric drift condition towards a small set. A similar result is also proved for a special version of the algorithm, used for probabilistic inference in Continuous Time Bayesian Networks.
Electron. J. Statist., Volume 11, Number 2 (2017), 4629-4648.
Received: November 2016
First available in Project Euclid: 18 November 2017
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 65C40: Computational Markov chains
Secondary: 65C05: Monte Carlo methods 60J27: Continuous-time Markov processes on discrete state spaces
Miasojedow, Błażej; Niemiro, Wojciech. Geometric ergodicity of Rao and Teh’s algorithm for Markov jump processes and CTBNs. Electron. J. Statist. 11 (2017), no. 2, 4629--4648. doi:10.1214/17-EJS1348. https://projecteuclid.org/euclid.ejs/1510974128