abc: an R package for approximate Bayesian computation (ABC)

abc: an R package for Approximate Bayesian Computation (ABC)

Background: Many recent statistical applications involve inference under complex models, where it is computationally prohibitive to calculate likelihoods but possible to simulate data. Approximate Bayesian Computation (ABC) is devoted to these complex models because it bypasses evaluations of the likelihood function using comparisons between observed and simulated summary statistics. Results: W...

Approximate Bayesian Computation (ABC) in R: A Vignette

A brief introduction to ABC 3 Parameter inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Cross-validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Model misclassification . . . . . . . . . . . . . ...

Approximate Bayesian Computation (ABC) in practice.

Understanding the forces that influence natural variation within and among populations has been a major objective of evolutionary biologists for decades. Motivated by the growth in computational power and data complexity, modern approaches to this question make intensive use of simulation methods. Approximate Bayesian Computation (ABC) is one of these methods. Here we review the foundations of ...

Introduction to Approximate Bayesian Computation (ABC)

DR-ABC: Approximate Bayesian Computation with Kernel-Based Distribution Regression

Performing exact posterior inference in complex generative models is often difficult or impossible due to an expensive to evaluate or intractable likelihood function. Approximate Bayesian computation (ABC) is an inference framework that constructs an approximation to the true likelihood based on the similarity between the observed and simulated data as measured by a predefined set of summary st...