This is a somewhat belated introduction of a package that we published on CRAN at the beginning of the year already, but I hadn’t found the time to blog about this earlier. In the R environment and beyond, a large number of packages exist that estimate posterior distributions via MCMC sampling, either for specific statistical models (e.g.…
The central quantity in Bayesian inference, the posterior, can usually not be calculated analytically, but needs to be estimated by numerical integration, which is typically done with a Monte-Carlo algorithm. The three main algorithm classes for doing so are Rejection sampling Markov-Chain Monte Carlo (MCMC) sampling Sequential Monte Carlo (SMC) sampling I have previously given…
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”…
Last week, I gave a seminar about MCMC chain analysis and convergence diagnostics with coda in R, and I thought a summary would make a nice post. Note: this post is about checking the convergence of the MCMC – a more recent post explains how to check the adequacy of model assumptions in a Bayesian…
NOTE: these instructions are outdated, an update has been posted here. WinBugs and its open source counterpart OpenBugs are the most commonly used software packages for evaluating the posterior distribution of a Bayesian model by means of a Markov Chain Monte Carlo algorithm (see e.g. my previous post for more details on MCMC algorithms). WinBugs…
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…
Motivation Statistical modeling with Bayesian methods has become increasingly popular in ecology, but the techniques required for doing so are not part of the standard curriculum in most Universities. Our aim for this course is to give a simple and practical introduction to Bayesian modeling for graduate students and early-stage researchers in ecology. Organizers: Björn…
While there are certainly good software packages out there to do the job for you, notably BUGS or JAGS, but also our own BayesianTools package with general-purpose MCMC samplers, it is instructive to program a simple MCMC yourself. In this post, I give an educational example of the Bayesian equivalent of a linear regression, sampled…
theoretical ecology 