Scalable Approximate Bayesian Computation for Growing Network Models via Extrapolated and Sampled Summaries.

ABSTRACT: Approximate Bayesian computation (ABC) is a simulation-based likelihood-free method applicable to both model selection and parameter estimation. ABC parameter estimation requires the ability to forward simulate datasets from a candidate model, but because the sizes of the observed and simulated datasets usually need to match, this can be computationally expensive. Additionally, since ABC inference is based on comparisons of summary statistics computed on the observed and simulated data, using computationally expensive summary statistics can lead to further losses in efficiency. ABC has recently been applied to the family of mechanistic network models, an area that has traditionally lacked tools for inference and model choice. Mechanistic models of network growth repeatedly add nodes to a n

SUBMITTER: Raynal L 

PROVIDER: S-EPMC9541316 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature