Many of the statistical models that could provide an accurate, interesting, and testable
explanation for the structure of a data set turn out to have intractable likelihood functions …

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 …

Abstract Approximate Bayesian Computation (ABC) methods, also known as likelihood-free
techniques, have appeared in the past ten years as the most satisfactory approach to …

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 …

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

Approximate Bayesian computation (ABC) is a popular approach to address inference
problems where the likelihood function is intractable, or expensive to calculate. To improve …

Abstract Sagittarius A*(Sgr A*) is the variable radio, near-infrared (NIR), and X-ray source
associated with accretion onto the Galactic center black hole. We present an analysis of the …

We propose a new approximate Bayesian computation (ABC) algorithm that aims at
minimizing the number of model runs for reaching a given quality of the posterior …

Stochasticity is a key characteristic of intracellular processes such as gene regulation and
chemical signalling. Therefore, characterizing stochastic effects in biochemical systems is …

Adapting the ABC Distance Function Page 1 Bayesian Analysis (2017) 12, Number 1, pp.
289–309 Adapting the ABC Distance Function Dennis Prangle ∗† Abstract. Approximate …