Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
*Corresponding author Email: email@example.com
Approximate Bayesian computation is an analysis approach that has arisen in response to the recent trend to collect data of very high dimension. This has led to many existing methods become intractable because of difficulties in calculating the likelihood function. Approximate Bayesian computation circumvents this issue by replacing calculation of the likelihood with a simulation step in which it is estimated in one way or another. In this review, we give an overview of the approximate Bayesian computation approach, by giving examples of some of the more popular specific forms of approximate Bayesian computation. We then discuss some of the areas of most active research and application in the field, specifically, choice of low-dimensional summaries of complex datasets and metrics for measuring similarity between observed and simulated data. Next, we consider the question of how to do model selection in an approximate Bayesian computation context. Finally, we discuss an area of growing prominence in the approximate Bayesian computation world, use of approximate Bayesian computation methods in genetic pathway inference.
We expect the rise of approximate Bayesian computation methods to continue, and we hope this will include the continued development of theory and machinery to guide the user in making some of the key choices discussed above.