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

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• 作者：David A. Kennedy ; Vanja Dukic ; Greg Dwyer
• 关键词：Birth–death model ; MCMC ; Parameter line ; search ; Survival ; time data ; Within ; host model
• 刊名：Environmental and Ecological Statistics
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：22
• 期：2
• 页码：247-274
• 全文大小：1,716 KB
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• 作者单位：David A. Kennedy (1) (2) (3)
  Vanja Dukic (4)
  Greg Dwyer (1)
  
  1. Department of Ecology and Evolution, University of Chicago, Chicago, IL, USA
  2. Center for Infectious Disease Dynamics, Pennsylvania State University, University Park, PA, USA
  3. Fogarty International Center, National Institutes of Health, Bethesda, MD, USA
  4. Department of Applied Mathematics, University of Colorado - Boulder, Boulder, CO, USA
  
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Life Sciences
  Ecology
  Statistics
  Mathematical Biology
  Evolutionary Biology
  
• 出版者：Springer Netherlands
• ISSN：1573-3009

文摘

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.