Approximate Bayesian Computation (ABC) is a likelihood‐free inference methodology that has revolutionised the way researchers tackle complex problems where the likelihood function is difficult or ...

Over the past ten years, Approximate Bayesian Computation (ABC) has become hugely popular to estimate the parameters of a model when the likelihood function cannot be computed in a reasonable amount ...

ABC (approximate Bayesian computation) is a general approach for dealing with models with an intractable likelihood. In this work, we derive ABC algorithms based on QMC (quasi-Monte Carlo) sequences.

A bioengineering group is bringing the worlds of computational modeling and experimentation closer together by developing a methodology to help analyze the wealth of imaging data provided by ...

Indirect inference (II) is a methodology for estimating the parameters of an intractable (generative) model on the basis of an alternative parametric (auxiliary) model that is both analytically and ...