Supplement to “A Comparative Review of Dimension Reduction Methods in Approximate
Bayesian Computation”. The supplement contains for each of the three examples a …

Developing mechanistic models has become an integral aspect of systems biology, as has
the need to differentiate between alternative models. Parameterizing mathematical models …

Abstract Motivation: Approximate Bayesian computation (ABC) methods provide an
elaborate approach to Bayesian inference on complex models, including model choice. Both …

Many modern statistical applications involve inference for complex stochastic models, where
it is easy to simulate from the models, but impossible to calculate likelihoods. Approximate …

Subclonal architectures are prevalent across cancer types. However, the temporal
evolutionary dynamics that produce tumor subclones remain unknown. Here we measure …

As modeling becomes a more widespread practice in the life sciences and biomedical
sciences, researchers need reliable tools to calibrate models against ever more complex …

Approximate Bayesian computation (ABC) or likelihood-free inference algorithms are used
to find approximations to posterior distributions without making explicit use of the likelihood …

Abstract Approximate Bayesian Computation (ABC) techniques are a suite of model fitting
methods which can be implemented without a using likelihood function. In order to use ABC …

Approximate Bayesian computation (ABC) has gained popularity over the past few years for
the analysis of complex models arising in population genetics, epidemiology and system …

Simulation-based methods for statistical inference have evolved dramatically over the past
50 years, keeping pace with technological advancements. The field is undergoing a new …