Understanding and predicting the ecological consequences of different management
alternatives is becoming increasingly important to support environmental management …

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

This study considers Bayesian calibration of hydrological models using streamflow
signatures and its implementation using Approximate Bayesian Computation (ABC). If the …

This study investigates Bayesian signature‐domain inference of hydrological models using
Approximate Bayesian Computation (ABC) algorithms, and compares it to “traditional” time …

Calibration of precipitation‐streamflow models to streamflow signatures is a promising
approach for streamflow prediction in ungauged basins (PUB). The estimation of parameter …

Bayesian Likelihood-Free Inference (LFI) approaches allow to obtain posterior distributions
for stochastic models with intractable likelihood, by relying on model simulations. In …

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 …

Infectious diseases are studied to understand their spreading mechanisms, to evaluate
control strategies and to predict the risk and course of future outbreaks. Because people …

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