Hi, Just to play a bit with the gganimate package, and to celebrate National Coupling Day, the above plot shows different couplings of two univariate Normal distributions, Normal(0,1) and Normal(2,1). That is, each point is a pair (x,y) where x follows a Normal(0,1) and y follows a Normal(2,1). Below I’ll recall briefly how each coupling
Hi all, This post deals with a strange phenomenon in R that I have noticed while working on unbiased MCMC. Reducing the problem to a simple form, consider the following code, which iteratively samples a vector ‘x’ and stores it in a row of a large matrix called ‘chain’ (I’ve kept the MCMC
Hello, An example often used in the ABC literature is the g-and-k distribution (e.g. reference below), which is defined through the inverse of its cumulative distribution function (cdf). It is easy to simulate from such distributions by drawing uniform variables and applying the inverse cdf to them. However, since there is no closed-form
Today I am going to introduce the moustache target distribution (moustarget distribution for brievety). Load some packages first. Let’s invoke the moustarget distribution. This defines a target distribution represented by a SVG file using RShapeTarget. The target probability density function is defined on and is proportional to on the segments described in the SVG files,
Besides having coded a pretty cool MCMC app in Javascript, this guy Rasmus Bååth has started the Bayesian first aid project. The idea is that if there’s an R function called blabla.test performing test “blabla”, there should be a function bayes.blabla.test performing a similar test in a Bayesian framework, and showing the output in a
Hey, To illustrate generally complex probability density functions on continuous spaces, researchers always use the same examples, for instance mixtures of Gaussian distributions or a banana shaped distribution defined on with density function: If we draw a sample from this distribution using MCMC we obtain a plot like this one: Clearly it doesn’t really look
The PAWL package (which I talked about there, and which implements the parallel adaptive Wang-Landau algorithm and adaptive Metropolis-Hastings for comparison) is now on CRAN! http://cran.r-project.org/web/packages/PAWL/index.html which means that within R you can easily install it by typing install.packages("PAWL") Isn’t that amazing? It’s just amazing. Kudos to the CRAN team for their quickness and their
Hi, Related to Julyan’s previous post, I want to share an easy way to access Google Maps API through R. And then we’ll stop about Google, otherwise it’ll look like we’re just looking for jobs. My problem was the following: … Continue reading →
![Density exploration and Wang-Landau algorithms [with R package]](https://statisfaction.files.wordpress.com/2011/09/mixture4kweightedpoints.png?w=300&h=300)
Hey, Since a new paper that I’ve co-written has appeared on arXiv, here is a quick post summarizing it. The paper is named: An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration and describes improvements over the Wang-Landau algorithm described … Continue reading →
Hi again, Recently, Julyan blogged about allocating memory first before modifying R objects. That’s a very useful trick that applies to most programming languages. Tonight I want to blog about something a bit more complicated and more specific to R, but which can lead to massive speed-ups: interfacing C/C++ within R codes. The rationale behind
