# Blog Archives

## CPU and GPU trends over time

January 25, 2011
By

GPUs seem to be all the rage these days. At the last Bayesian Valencia meeting, Chris Holmes gave a nice talk on how GPUs could be leveraged for statistical computing. Recently Christian Robert arXived a paper with parallel computing firmly in mind. In two weeks time I’m giving an internal seminar on using GPUs for

## Parsing and plotting time series data

January 15, 2011
By

This morning I came across a post which discusses the differences between scala, ruby and python when trying to analyse time series data. Essentially, there is a text file consisting of times in the format HH:MM and we want to get an idea of its distribution. Tom discusses how this would be a bit clunky

## Statistical podcast: Random and Pseudorandom

January 14, 2011
By

This morning when I downloaded the latest version of In our time, I was pleased to see that this weeks topic was “Random and Peudorandom.” If you’re not familiar with “In our time”, then I can I definitely recommend the series. Each week three academics and Melvyn Bragg discuss a particular topic from history, science,

## Survival paper (update)

January 13, 2011
By

In a recent post, I discussed some  statistical consultancy I was involved with. I was quite proud of the nice ggplot2 graphics I had created. The graphs nicely summarised the main points of the paper: I’ve just had the proofs from the journal, and next to the graphs there is the following note: It is

## Random variable generation (Pt 3 of 3)

January 12, 2011
By
$Random variable generation (Pt 3 of 3)$

Ratio-of-uniforms This post is based on chapter 1.4.3 of Advanced Markov Chain Monte Carlo.  Previous posts on this book can be found via the  AMCMC tag. The ratio-of-uniforms was initially developed by Kinderman and Monahan (1977) and can be used for generating random numbers from many standard distributions. Essentially we transform the random variable of

## R programming books

December 21, 2010
By

My sabbatical is rapidly coming to an end, and I have to start thinking more and more about teaching. Glancing over my module description for the introductory computational statistics course I teach, I noticed that it’s a bit light on recommend/background reading. In fact it has only two books: A first course in statistical programming

## Logical operators in R

December 14, 2010
By

In R, the operators “|” and “&” indicate the logical operations OR and AND. For example, to test if x equals 1 and y equals 2 we do the following: > x = 1; y = 2 > (x == 1) & (y == 2) TRUE However, if you are used to programming in

## New paper: Survival analysis

December 8, 2010
By

Each year I try to carry out some statistical consultancy to give me experience in other areas of statistics and also to provide teaching examples. Last Christmas I was approached by a paediatric consultant from the RVI who wanted to carry out prospective survival analysis. The consultant, Bruce  Jaffray, had performed Nissen fundoplication surgery on

## Random variable generation (Pt 2 of 3)

December 2, 2010
By
$Random variable generation (Pt 2 of 3)$

Acceptance-rejection methods This post is based on chapter 1.4 of Advanced Markov Chain Monte Carlo. Another method of generating random variates from distributions is to use acceptance-rejection methods. Basically to generate a random number from , we generate a RN from an envelope distribution , where .  The acceptance-rejection algorithm is as follows: Repeat until

## Random variable generation (Pt 1 of 3)

November 28, 2010
By
$Random variable generation (Pt 1 of 3)$

As I mentioned in a recent post, I’ve just received a copy of Advanced Markov Chain Monte Carlo Methods. Chapter 1.4 in the book (very quickly) covers random variable generation. Inverse CDF Method A standard algorithm for generating random numbers is the inverse cdf method. The continuous version of the algorithm is as follows: 1.