Widening and clustering techniques allowing the use of monotone CFTP algorithm
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Mohamed Yasser Bounnite
Abstract
The standard Coupling From The Past (CFTP) algorithm is an interesting tool to sample from exact stationary distribution of a Markov chain. But it is very expensive in time consuming for large chains. There is a monotone version of CFTP, called MCFTP, that is less time consuming for monotone chains. In this work, we propose two techniques to get monotone chain allowing use of MCFTP: widening technique based on adding two fictitious states and clustering technique based on partitioning the state space in clusters. Usefulness and efficiency of our approaches are showed through a sample of Markov Chain Monte Carlo simulations.
Funding source: Ibn-al-Banna Laboratory of Mathematics and Applications (LIBMA) at Cadi Ayyad University
Funding source: Hassan II Academy of Sciences and Technology
© 2015 by De Gruyter
Artikel in diesem Heft
- Frontmatter
- The parallel replica method for computing equilibrium averages of Markov chains
- Mathematical verification of the Monte Carlo maximum cross-section technique
- Infinite-dimensional Monte-Carlo integration
- Widening and clustering techniques allowing the use of monotone CFTP algorithm
- Constructing positivity preserving numerical schemes for the two-factor CIR model
- Simulation of random variates with the Morgenstern distribution
Artikel in diesem Heft
- Frontmatter
- The parallel replica method for computing equilibrium averages of Markov chains
- Mathematical verification of the Monte Carlo maximum cross-section technique
- Infinite-dimensional Monte-Carlo integration
- Widening and clustering techniques allowing the use of monotone CFTP algorithm
- Constructing positivity preserving numerical schemes for the two-factor CIR model
- Simulation of random variates with the Morgenstern distribution
