Name
Abstract | ||
|---|---|---|
The preconditioned Crank-Nicolson (pCN) method is a Markov Chain Monte Carlo (MCMC) scheme, specifically designed to perform Bayesian inferences in function spaces. Unlike many standard MCMC algorithms, the pCN method can preserve the sampling efficiency under the mesh refinement, a property referred to as being dimension independent. In this work we consider an adaptive strategy to further improve the efficiency of pCN. In particular we develop a hybrid adaptive MCMC method: the algorithm performs an adaptive Metropolis scheme in a chosen finite dimensional subspace, and a standard pCN algorithm in the complement space of the chosen subspace. We show that the proposed algorithm satisfies certain important ergodicity conditions. Finally with numerical examples we demonstrate that the proposed method has competitive performance with existing adaptive algorithms. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1137/16M1082950 | SIAM-ASA JOURNAL ON UNCERTAINTY QUANTIFICATION |
Keywords | Field | DocType |
adaptive Metropolis,Bayesian inference,function space,inverse problems,Markov chain Monte Carlo | Ergodicity,Mathematical optimization,Function space,Adaptive strategies,Markov chain Monte Carlo,Subspace topology,Algorithm,Sampling (statistics),Statistics,Mathematics,Bayesian probability | Journal |
Volume | Issue | ISSN |
5 | 1 | 2166-2525 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingping Zhou | 1 | 0 | 0.34 |
| Zixi Hu | 2 | 3 | 0.91 |
| Zhewei Yao | 3 | 31 | 10.58 |
| Jinglai Li | 4 | 13 | 3.90 |