Adaptive approximate Bayesian computation

Adaptive approximate Bayesian computation for complex models

Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation

Methods of Approximate Bayesian computation (ABC) are increasingly used for analysis of complex models. A major challenge for ABC is over-coming the often inherent problem of high rejection rates in the accept/reject methods based on prior:predictive sampling. A number of recent developments aim to address this with extensions based on sequential Monte Carlo (SMC) strategies. We build on this h...

An adaptive sequential Monte Carlo method for approximate Bayesian computation

Approximate Bayesian computation (ABC) is a popular approach to address inference problems where the likelihood function is intractable, or expensive to calculate. To improve over Markov chain Monte Carlo (MCMC) implementations of ABC, the use of sequential Monte Carlo (SMC) methods has recently been suggested. Effective SMC algorithms that are currently available for ABC have a computational c...

Fundamentals and Recent Developments in Approximate Bayesian Computation

Bayesian inference plays an important role in phylogenetics, evolutionary biology, and in many other branches of science. It provides a principled framework for dealing with uncertainty and quantifying how it changes in the light of new evidence. For many complex models and inference problems, however, only approximate quantitative answers are obtainable. Approximate Bayesian computation (ABC) ...

Approximate Bayesian Computation using Markov Chain Monte Carlo simulation: DREAM(ABC)

The quest for a more powerful method for model evaluation has inspired Vrugt and Sadegh (2013) to introduce ‘‘likelihood-free’’ inference as vehicle for diagnostic model evaluation. This class of methods is also referred to as Approximate Bayesian Computation (ABC) and relaxes the need for a residualbased likelihood function in favor of one or multiple different summary statistics that exhibit ...