Al3c: high-performance software for parameter inference using Approximate Bayesian Computation.

ABSTRACT:

Motivation

The development of Approximate Bayesian Computation (ABC) algorithms for parameter inference which are both computationally efficient and scalable in parallel computing environments is an important area of research. Monte Carlo rejection sampling, a fundamental component of ABC algorithms, is trivial to distribute over multiple processors but is inherently inefficient. While development of algorithms such as ABC Sequential Monte Carlo (ABC-SMC) help address the inherent inefficiencies of rejection sampling, such approaches are not as easily scaled on multiple processors. As a result, current Bayesian inference software offerings that use ABC-SMC lack the ability to scale in parallel computing environments.

Results

We present al3c, a C++ framework for implementing

SUBMITTER: Stram AH 

PROVIDER: S-EPMC4626746 | biostudies-literature | 2015 Nov

REPOSITORIES: biostudies-literature