Possibly the last post on random number generation by Kinderman and Monahan’s (1977) ratio-of-uniform method. After fiddling with the Gamma(a,1) distribution when a

The riddle set by The Riddler of last week sums up as follows: Within a population of size N, each individual in the population independently selects another individual. All individuals selected at least once are removed and the process iterates until one or zero individual is left. What is the ...

By some piece of luck I came across a paper by the late George Marsaglia, genial contributor to the field of simulation, and Wai Wan Tang, entitled The Monty Python method for generating random variables. As shown by the below illustration, the concept is to flip the piece H outside ...

A rather obscure question on Metropolis-Hastings algorithms on X Validated ended up being about our first illustration in Introducing Monte Carlo methods with R. And exposing some inconsistencies in the following example… Example 7.2 is based on a [toy] joint Beta x Binomial target, which leads to a basic Gibbs sampler. ...

This afternoon, one of my Monte Carlo students at ENSAE came to me with an exercise from Monte Carlo Statistical Methods that I did not remember having written. And I thus “charged” George Casella with authorship for that exercise! Exercise 3.3 starts with the usual question (a) about the (Binomial) precision ...

Being still puzzled (!) by the ratio-of-uniform approach, mostly failing to catch its relevance for either standard distributions in a era when computing a cosine or an exponential is negligible, or non-standard distributions for which computing bounds and boundaries is out-of-reach, I kept searching for solutions that would include unbounded densities ...

Following a question on X Validated, I became aware of the following descriptions of the pros and cons of Bayesian analysis, as perceived by whoever (Tim Arnold?) wrote SAS/STAT(R) 9.2 User’s Guide, Second Edition. I replied more specifically on the point It [Bayesian inference] provides inferences that are ...

Following my earlier post on Kinderman’s and Monahan’s (1977) ratio-of-uniform method, I must confess I remain quite puzzled by the approach. Or rather by its consequences. When looking at the set A of (u,v)’s in R⁺×X such that 0≤u²≤ƒ(v/u), as discussed in the previous ...

As discussed in the previous entry, there are two interpretations to this question from The Riddler: “…how long is the longest path a knight can travel on a standard 8-by-8 board without letting the path intersect itself?” as to what constitutes a path. As a (terrible) chess player, I would ...

One approach to random number generation that had always intrigued me is Kinderman and Monahan’s (1977) ratio-of-uniform method. The method is based on the result that the uniform distribution on the set A of (u,v)’s in R⁺xX such that 0≤u²≤ƒ(v/u) induces the distribution with density ...

David Rossell and Francisco Rubio (both from Warwick) arXived a month ago a paper on non-normal variable selection. They use two-piece error models that preserve manageable inference and allow for simple computational algorithms, but also characterise the behaviour of the resulting variable selection process under model misspecification. Interestingly, they show ...

The Riddler of this week had a riddle that is a variation of the knight tour problem, namely “…how long is the longest path a knight can travel on a standard 8-by-8 chessboard without letting the path intersect itself?” the riddle being then one of a self-avoiding random walk [kind]… ...

Following my earlier post on Delyon and Portier’s proposal to replacing the true importance distribution ƒ with a leave-one-out (!) kernel estimate in the importance sampling estimator, I ran a simple one-dimensional experiment to compare the performances of the traditional method with this alternative. The true distribution is a N(0,½) with ...