Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models.

ABSTRACT: Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on sever

SUBMITTER: Daly AC 

PROVIDER: S-EPMC5636270 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature