As I said before, I’m firmly siding with Andrew Gelman (see e.g. here) in that model checking is dangerously neglected in Bayesian practice. The philosophical criticism against “rejecting” models (double-using data etc. etc.) is all well, but when using Bayesian methods in practice, I see few sensible alternatives to residual checks (both guessing a model and…
I just released a small R package that I have been working on for a while. The motivation for this package came from the observation that I kept on receiving questions about residual checks for GLMMs. The problem that people have is that they have fitted their GLMM, maybe they tested it for overdispersion and…
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…
I have blogged a few days a ago about a study by Kiju Jung that suggested that implicit bias leads people to underestimate the danger of female-named hurricanes. The study used historical data to demonstrate a correlation between femininity and death-toll, and subsequent experiments seemed to show that people indeed estimate hurricanes to be less…
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…
A colleague had a question about sampling design and we didn’t find a good answer … so, if you like to solve riddles, you might like that one: We want to distribute n=3 plant species across k=12 x m=12 grid cells, in a way that no individual has another individual of it’s own species in…
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…
