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…
Yesterday, I gave my first webinar, or online lecture if you want. The occasion was that a few people from another university asked me if I could give an introductory lecture about Bayesian statistics, and because traveling would have cost me a full day, I suggested doing a “virtual visit” instead. It’s a funny coincidence…
Our new perspective paper on “Connecting dynamic vegetation models to data – an inverse perspective” is now available online at the Journal of Biogeography (see also this post by my coauthor James Dyke). The paper is a part of a special issue on distribution models “The ecological niche as a window to biodiversity” that will…
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…
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 