Hi all, On top of recommending the excellent autobiography of Stanislaw Ulam, this post is about using the software Stan, but not directly to perform inference, instead to obtain R functions to evaluate a target’s probability density function and its gradient. With which, one can implement custom methods, while still benefiting from the great workContinue reading “Coding algorithms in R for models written in Stan”

# Category Archives: R

## Couplings of Normal variables

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 couplingContinue reading “Couplings of Normal variables”

## Sub-Gaussian property for the Beta distribution (part 3, final)

In this third and last post about the Sub-Gaussian property for the Beta distribution [1] (post 1 and post 2), I would like to show the interplay with the Bernoulli distribution as well as some connexions with optimal transport (OT is a hot topic in general, and also on this blog with Pierre’s posts on WassersteinContinue reading “Sub-Gaussian property for the Beta distribution (part 3, final)”

## Sub-Gaussian property for the Beta distribution (part 2)

As a follow-up on my previous post on the sub-Gaussian property for the Beta distribution [1], I’ll give here a visual illustration of the proof. A random variable with finite mean is sub-Gaussian if there is a positive number such that: We focus on X being a Beta random variable. Its moment generating function is known asContinue reading “Sub-Gaussian property for the Beta distribution (part 2)”

## nrow, references and copies

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 MCMCContinue reading “nrow, references and copies”

## New R user community in Grenoble, France

Nine R user communities already exist in France and there is a much large number of R communities around the world. It was time for Grenoble to start its own! The goal of the R user group is to facilitate the identification of local useRs, to initiate contacts, and to organise experience and knowledge sharing sessions.Continue reading “New R user community in Grenoble, France”

## Likelihood calculation for the g-and-k distribution

Hello, An example often used in the ABC literature is the g-and-k distribution (e.g. reference [1] 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-formContinue reading “Likelihood calculation for the g-and-k distribution”

## 3D density plot in R with Plotly

In Bayesian nonparametrics, many models address the problem of density regression, including covariate dependent processes. These were settled by the pioneering works by [current ISBA president] MacEachern (1999) who introduced the general class of dependent Dirichlet processes. The literature on dependent processes was developed in numerous models, such as nonparametric regression, time series data, meta-analysis, to cite butContinue reading “3D density plot in R with Plotly”

## Statistics journals network

Xian blogged recently on the incoming RSS read paper: Statistical Modelling of Citation Exchange Between Statistics Journals, by Cristiano Varin, Manuela Cattelan and David Firth. Following the last JRSS B read paper by one of us! The data that are used in the paper (and can be downloaded here) are quite fascinating for us, academics fascinated by academic rankings,Continue reading “Statistics journals network”

## momentify R package at BAYSM14

I presented an arxived paper of my postdoc at the big success Young Bayesian Conference in Vienna. The big picture of the talk is simple: there are situations in Bayesian nonparametrics where you don’t know how to sample from the posterior distribution, but you can only compute posterior expectations (so-called marginal methods). So e.g. you cannot provide credible intervals.Continue reading “momentify R package at BAYSM14”