With all my recent focus on reporting and visualization, you might think that I have the investments all figured out. Unfortunately, that is not the case, and I will resume more standard investment and systems posts soon. I did want to shar...

If you can write the likelihood function for your model, MHadaptive will take care of the rest (ie. all that MCMC business). I wrote this R package to simplify the estimation of posterior distributions of arbitrary models. Here’s how it works: 1) Define your model (ie the likelihood * prior). In this example, lets build

Erik Sigur, Information Technologist for the Department of Statistics and Probability at Michigan State University, writes at ReadWriteWeb about using Revolution R Enterprise to provide high-performance computation in R to the researchers in his department: Our search for a more effective version of R ultimately brought us to a product called Revolution R Enterprise by Revolution Analytics, which provides...

(Almost) every introductory course in probability introduces conditional probability using the famous Monte Hall problem. In a nutshell, the problem is one of deciding on a best strategy in a simple game. In the game, the contestant is asked to select one of three doors. Behind one of the doors is a great prize (free