Articles by Florian Hartig

A simple explanation of rejection sampling in R

April 22, 2015 | Florian Hartig

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) ... [Read more...]

Explaining the ABC-Rejection Algorithm in R

June 2, 2014 | Florian Hartig

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, ... [Read more...]

Sampling design combinatorics

January 14, 2014 | Florian Hartig

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 ... [Read more...]

A simple Approximate Bayesian Computation MCMC (ABC-MCMC) in R

July 15, 2012 | Florian Hartig

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 ... [Read more...]

A simple Metropolis-Hastings MCMC in R

September 17, 2010 | Florian Hartig

While there are certainly good software packages out there to do the job for you, notably BUGS or JAGS, 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 by an MCMC with Metropolis-Hastings ... [Read more...]

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