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…
I just arXived the final version of our technical note “Approximate Bayesian parameterization of a process-based tropical forest model” (coauthored with Claudia Dislich, Thorsten Wiegand and Andreas Huth) that is now accepted and will appear in Biogeosciences soon. There have been a few minor changes in response to reviewer comments on a discussion version of…
There’s been too many interesting papers in the last weeks for a lone blogger to cover, so I’m glad that other interesting stuff has been discussed elsewhere, e.g. by EEB & flow on the controversy around “Novel Ecosystems”, NIBMS explaining us why there may be no species below 1 mm, and Nature reporting on the…
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),…
A comment on a recent post gave me the motivation to try out the new EasyABC package for R, developed by Franck Jabot, Thierry Faure, Nicolas Dumoulin and maintained by Nicolas Dumoulin. Approximate Bayesian Computation (ABC) is a relatively new method that allows treating any stochastic model (IBM, stochastic population model, …) in a statistical…
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”…
It can scarcely be denied that the supreme goal of all theory is to make the irreducible basic elements as simple and as few as possible without having to surrender the adequate representation of a single datum of experience. (From “On the Method of Theoretical Physics,” the Herbert Spencer Lecture, Oxford, June 10, 1933.) I…
In a proverbial “mammoth project”, Lorenzen and colleagues report in this weeks issue of Nature on the responses of six Late Quaternary megafauna herbivores (woolly rhinoceros, woolly mammoth, horse, reindeer/caribou, bison and musk ox) to climate and humans. The authors draw on various data sources, in particular species distribution models and ancient DNA, to “reconstruct…
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…
theoretical ecology 