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 …

Abstract Approximate Bayesian Computation (ABC) methods are increasingly used for
inference in situations in which the likelihood function is either computationally costly or …

Approximate Bayes computations (ABC) are used for parameter inference when the
likelihood function of the model is expensive to evaluate but relatively cheap to sample from …

We are living in the big data era, as current technologies and networks allow for the easy
and routine collection of data sets in different disciplines. Bayesian Statistics offers a flexible …

Abstract Approximate Bayesian Computation (ABC) methods have become a “mainstream”
statistical technique in the past decade, following the realisation by statisticians that they are …

An overview on Approximate Bayesian computation\* Page 1 ESAIM: PROCEEDINGS, January
2014, Vol. 44, p. 291-299 SMAI Groupe MAS – Journées MAS 2012 – Session thématique AN …

ABSTRACT 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 …

Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it
is feasible to simulate pseudo-data from the model at each iteration. However, the …

Analytically or computationally intractable likelihood functions can arise in complex
statistical inferential problems making them inaccessible to standard Bayesian inferential …

The Lotka-Volterra model (Lotka, 1925; Volterra, 1927) describes two interacting
populations and in their original ecological setting representing predators and prey. Since …