Bayesian parameter inference and model selection by population annealing in systems biology.

ABSTRACT: Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population a

SUBMITTER: Murakami Y 

PROVIDER: S-EPMC4121267 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature