Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)

Simulation-based estimation of mean and standard deviation for meta-analysis via Approximate Bayesian Computation (ABC)

BACKGROUND When conducting a meta-analysis of a continuous outcome, estimated means and standard deviations from the selected studies are required in order to obtain an overall estimate of the mean effect and its confidence interval. If these quantities are not directly reported in the publications, they must be estimated from other reported summary statistics, such as the median, the minimum, ...

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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 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)