A critical task in modeling is to determine how well the theoretical assumptions encoded in a
model account for observations. Bayesian methods are an ideal framework for doing just …

This chapter surveys the various forms of approximate Bayesian computation (ABC)
algorithms that have been developed to sample from pABC. The earliest ABC samplers …

Abstract Approximate Bayesian Computation (ABC) has become increasingly prominent as
a method for conducting parameter inference in a range of challenging statistical problems …

Approximate Bayesian computation (ABC) has become one of the major tools of likelihood-
free statistical inference in complex mathematical models. Simultaneously, stochastic …

This paper provides a new approximate Bayesian computation (ABC) algorithm with
reduced hyper‐parameter scaling and its application to nonlinear structural model …

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 …

Approximate Bayesian Inference in Semiparametric Copula Models Page 1 Bayesian
Analysis (2017) 12, Number 4, pp. 991–1016 Approximate Bayesian Inference in …

We propose a method to combine N portfolio strategies by optimizing a given utility function
U (⋅) U (·). The method does not rely on any distributional assumption, could be easily …

This article introduces a Bayesian method for testing the axioms of additive conjoint
measurement. The method is based on an importance sampling algorithm that performs …