Combining principal component analysis with parameter line-searches to improve the efficacy of Metropolis–Hastings MCMC

David A. Kennedy, Vanja Dukic, Greg Dwyer

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

When Markov chain Monte Carlo (MCMC) algorithms are used with complex mechanistic models, convergence times are often severely compromised by poor mixing rates and a lack of computational power. Methods such as adaptive algorithms have been developed to improve mixing, but these algorithms are typically highly sophisticated, both mathematically and computationally. Here we present a nonadaptive MCMC algorithm, which we term line-search MCMC, that can be used for efficient tuning of proposal distributions in a highly parallel computing environment, but that nevertheless requires minimal skill in parallel computing to implement. We apply this algorithm to make inferences about dynamical models of the growth of a pathogen (baculovirus) population inside a host (gypsy moth, Lymantria dispar). The line-search MCMC appeal rests on its ease of implementation, and its potential for efficiency improvements over classical MCMC in a highly parallel setting, which makes it especially useful for ecological models.

Original languageEnglish (US)
Article number3
Pages (from-to)247-274
Number of pages28
JournalEnvironmental and Ecological Statistics
Volume22
Issue number2
DOIs
StatePublished - Jun 22 2015

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Environmental Science(all)
  • Statistics, Probability and Uncertainty

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