Random number generation is typically a two step process: first generate a uniformly distributed value, then transform that value to have the desired distribution. The former is the hard part, but also the part more likely to have been done for you in a library. The latter is relatively easy in principle, though some distributions […]
Recently, I saw a reference to an interesting piece from 2013 by Peter Grogono, a computer scientist now retired from Concordia University. It's to do with checking the "quality" of a (pseudo-) random number generator.Specifically, Peter discusses what...
Recently, I saw a reference to an interesting piece from 2013 by Peter Grogono, a computer scientist now retired from Concordia University. It's to do with checking the "quality" of a (pseudo-) random number generator.Specifically, Peter discusses what...
Past posts on this blog have included several relating to Monte Carlo simulation - e.g., see here, here, and here.Recently I came across a great article by Matthew Sigal and Philip Chalmers in the Journal of Statistics Education. It's titled, "Play it ...
Past posts on this blog have included several relating to Monte Carlo simulation - e.g., see here, here, and here.Recently I came across a great article by Matthew Sigal and Philip Chalmers in the Journal of Statistics Education. It's titled, "Play it ...
M. E. O’Neill’s PCG family of random number generators looks very promising. It appears to have excellent statistical and cryptographic properties. And it takes remarkably little code to implement. (PCG stands for Permuted Congruential Generator.) The journal article announcing PCG gives the results of testing it with the TestU01 test suite. I wanted to try it out […]
I’ve recently delivered a three-day intensive short-course on Scala for statistical computing and data science. The course seemed to go well, and the experience has convinced me that Scala should be used a lot more by statisticians and data scientists for a range of problems in statistical computing. In particular, the simplicity of writing fast … Continue reading Statistical computing with Scala free on-line course
Monte Carlo simulation is an essential tool that econometricians use a great deal. For an introduction to some aspects of Monte Carlo simulation, see my earlier posts here, here, and here. There are some follow-up posts on this comi...
Introduction This weekend I’ve been preparing some material for my upcoming Scala for statistical computing short course. As part of the course, I thought it would be useful to walk through how to think about and structure MCMC codes, and in particular, how to think about MCMC algorithms as infinite streams of state. This material … Continue reading MCMC as a Stream
Introduction People regularly ask me about books and other resources for getting started with Scala for statistical computing and data science. This post will focus on books, but it’s worth briefly noting that there are a number of other resources available, on-line and otherwise, that are also worth considering. I particularly like the Coursera course … Continue reading Books on Scala for statistical computing and data science
