Bayesian experimental design is a fast growing area of research with many real‐world
applications. As computational power has increased over the years, so has the development …

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

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 …

Having the ability to work with complex models can be highly beneficial. However, complex
models often have intractable likelihoods, so methods that involve evaluation of the …

Ecology Letters (2011) 14: 816–827 Abstract Statistical models are the traditional choice to
test scientific theories when observations, processes or boundary conditions are subject to …

Approximate Bayesian computation (ABC) have become an essential tool for the analysis of
complex stochastic models. Grelaud et al.[(2009) Bayesian Anal 3: 427–442] advocated the …

Recent decades have seen enormous improvements in computational inference for
statistical models; there have been competitive continual enhancements in a wide range of …

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 …

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 …