2018/03/06 by Christopher Drovandi, Christopher C Drovandi, Drovandi, Christopher C
Chemistry · Computer Science · Mathematics · #Bayesian Methods and Mixture Models #Bayesian Modeling and Causal Inference #Computation (stat.CO) #FOS: Computer and information sciences #Methodology (stat.ME) #Spectroscopy and Chemometric Analyses #stat.CO #stat.ME
paper · pdf · doi:10.48550/arxiv.1803.01999
arxiv created 2018/03/06 · openalex publication_date 2018/03/06 · arxiv updated 2018/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This chapter will appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). Indirect inference (II) is a classical likelihood-free approach that pre-dates the main developments of ABC and relies on simulation from a parametric model of interest to determine point estimates of the parameters. It is not surprising then that some likelihood-free Bayesian approaches have harnessed the II literature. This chapter provides an introduction to II and details the connections between ABC and II. A particular focus is placed on the use of an auxiliary model with a tractable likelihood function, an approach commonly adopted in the II literature, to facilitate likelihood-free Bayesian inferences.