Approximate Bayesian Computation (ABC) is an umbrella term for a class of algorithms and ideas that allow performing an approximate estimation of the likelihood / posterior for stochastic simulation models when the likelihood cannot be explicitly calculated (intractable likelihood). To give you the idea in a nutshell: to approximate the likelihood, consider that for a…
Biogeosciences just published a discussion paper on a project I have been working on for quite a while now, namely on likelihood approximations for stochastic forest models (see also our review on the topic of simulation-based inference in general). From the technical side, it may be interesting that we don’t use Approximate Bayesian Computation (ABC),…
Approximate Bayesian Computing and similar techniques, which are based on calculating approximate likelihood values based on samples from a stochastic simulation model, have attracted a lot of attention in the last years, owing to their promise to provide a general statistical technique for stochastic processes of any complexity, without the limitations that apply to “traditional”…
Our new review on statistical inference for stochastic simulation models is now available at Ecology Letters. The paper discusses how approximate likelihood functions can be generated from stochastic simulations. Such simulation-based likelihood approximations are a very general and powerful tool because they allow us to apply statistical methods to a large class of models such…
