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.
All Science Journal Classification (ASJC) codes
- Statistics and Probability
- General Environmental Science
- Statistics, Probability and Uncertainty