On predictive inference for intractable models via approximate Bayesian computation.

ABSTRACT: Approximate Bayesian computation (ABC) is commonly used for parameter estimation and model comparison for intractable simulator-based statistical models whose likelihood function cannot be evaluated. In this paper we instead investigate the feasibility of ABC as a generic approximate method for predictive inference, in particular, for computing the posterior predictive distribution of future observations or missing data of interest. We consider three complementary ABC approaches for this goal, each based on different assumptions regarding which predictive density of the intractable model can be sampled from. The case where only simulation from the joint density of the observed and future data given the model parameters can be used for inference is given particular attention and it is shown

SUBMITTER: Jarvenpaa M 

PROVIDER: S-EPMC9911513 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature