Likelihood-free nested sampling for parameter inference of biochemical reaction networks.

ABSTRACT: The development of mechanistic models of biological systems is a central part of Systems Biology. One major challenge in developing these models is the accurate inference of model parameters. In recent years, nested sampling methods have gained increased attention in the Systems Biology community due to the fact that they are parallelizable and provide error estimates with no additional computations. One drawback that severely limits the usability of these methods, however, is that they require the likelihood function to be available, and thus cannot be applied to systems with intractable likelihoods, such as stochastic models. Here we present a likelihood-free nested sampling method for parameter inference which overcomes these drawbacks. This method gives an unbiased estimator of the Bay

SUBMITTER: Mikelson J 

PROVIDER: S-EPMC7577508 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature