**A**n interesting case on X validated of someone puzzled by the simulation (and variance) of the random variable 1/X when being able to simulate X. And being surprised at the variance of the ratio being way larger than the variances of both numerator and denominator.

## Archive for gamma distribution

## 1 / duh?!

Posted in Books, R, Statistics, University life with tags cross validated, gamma distribution, inverse Gamma distribution, Monte Carlo, Moschopoulos distribution, simulation, teaching, unconscious statistician on September 28, 2021 by xi'an## Monty Python generator

Posted in Books, Kids, pictures, R, Statistics, University life with tags gamma distribution, George Marsaglia, John Cleese, Monte Python, Monty Python, pseudo-random generator, silly walks, simulation on November 23, 2016 by xi'an**B**y 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 the rectangle back inside the rectangle, exploiting the remaining area above the density. The fantastic part is actually that “since areas of the rectangle add to 1, the slim in-between area is exactly the tail area”! So the tiny bit between G and the flipped H is the remaining tail.In the case of a Gamma Ga(a,1) variate, the authors express this variate as the transform of another variate with a nearly symmetry density, on which the Monty Python method applies. The transform is

with -√16a<x. The second nice trick is that the density of x is provided for free by the Gamma Ga(a,1) density and the transform, thanks to the change of variable formula. One lingering question is obviously how to handle the tail part. This is handled separately in the paper, with a rather involved algorithm, but since the area of the tail is tiny, a mere 1.2% in the case of the Gaussian density, this instance occurs rarely. Very clever if highly specialised! *(The case of a<1 has to be processed by the indirect of multiplying a Ga(a+1,1) by a uniform variate to the power 1/a.)*

I also found out that there exists a Monte Python software, which is an unrelated Monte Carlo code in python [hence the name] for cosmological inference. Including nested sampling, unsurprisingly.

## mixtures as exponential families

Posted in Kids, Statistics with tags Annals of Mathematical Statistics, cross validated, Dennis Lindley, exponential families, gamma distribution, label switching, location-scale parameterisation, mixtures of distributions on December 8, 2015 by xi'an**S**omething I had not realised earlier and that came to me when answering a question on X validated about the scale parameter of a Gamma distribution. Following an earlier characterisation by Dennis Lindley, Ferguson has written a famous paper characterising location, scale and location-scale families within exponential families. For instance, a one-parameter location exponential family is necessarily the logarithm of a power of a Gamma distribution. What I found surprising is the equivalent for one-parameter scale exponential families: they are necessarily mixtures of positive and negative powers of Gamma distributions. This is surprising because a mixture does not seem to fit within the exponential family representation… I first thought Ferguson was using a different type of mixtures. Or of exponential family. But, after checking the details, it appears that the mixture involves a component on ℜ⁺ and another component on ℜ⁻ with a potential third component as a Dirac mass at zero. Hence, it only nominally writes as a mixture and does not offer the same challenges as a regular mixture. Not label switching. No latent variable. Having mutually exclusive supports solves all those problems and even allows for an indicator function to permeate through the exponential function… (Recall that the special mixture processed in Rubio and Steel also enjoys this feature.)

## Le Monde puzzle [#847]

Posted in Books, Kids, R, Statistics with tags dice, gamma distribution, Le Monde, mathematical puzzle on December 29, 2013 by xi'an**A**nother X’mas Le Monde mathematical puzzle:

A regular dice takes the values 4, 8 and 2 on three adjacent faces. Summit values are defined by the product of the three connected faces, e.g., 64 for the above. What values do the three other faces take if the sum of the eight summit values is 1768?

**H**ere is the simple R code I used to find a solution:

summi=function(x){ #(x[1],x[2],x[3]) opposed to (4,8,2) sum(outer(c(2,x[1]),outer(c(8,x[2]),c(4,x[3]),"*"),"*"))} faces=matrix(sample(1:20,3*10^4,rep=T),ncol=3) resum=apply(faces,1,summi) sol=faces[resum==1768,]

with the result:

> sol [,1] [,2] [,3] [1,] 2 18 13 [2,] 2 18 13 [3,] 2 18 13 [4,] 6 5 13

which means the missing faces are (6,5,13) since the puzzle also imposed all faces were different. The following histogram of the sample of sums shows a reasonable gamma G(1.9,1763) fit.

## new typos in Monte Carlo Statistical Methods

Posted in Books, Statistics, University life with tags exponential distribution, gamma distribution, importance sampling, Metropolis-Hastings algorithms, Monte Carlo Statistical Methods, scale, simulation, truncated normal, typos on December 7, 2011 by xi'an**T**hanks to Jay Bartroff for pointing out those typos after teaching from ** Monte Carlo Statistical Methods**:

- On page 52, the gamma
*Ga(α, β)*distribution uses*β*as a rate parameter while in other places it is a scale parameter, see, e.g. eqn (2.2)*[correct, I must say the parameterisation of the gamma distribution is a pain and, while we tried to homogenise the notation with the second parameter being the rate, there are places like this where either the rate convention (as in the exponential distribution) or the scale convention (as in the generation) is the natural one…]*

- Still on page 52, in Example 2.20, truncated normals are said to be discussed after Example 1.5, but they’re not.
*[There is a mention made of constrained parameters right after but this is rather cryptic!]*

- On page 53, the ratio
*f/g*following the second displayed eqn is missing some terms_{α}*[or, rather, the equality sign should be a proportional sign]*

- Still on page 53, in eqn (2.11), the whole expression, rather than the square root, should be divided by 2
*[yes, surprising typo given that it was derived correctly in the original paper!]*

- On page 92, the exact constraint is that supp(g) actually needs only contain the intersection of supp(f) and supp(h), such as when approximating tail probabilities
*[correct if the importance sampling method is only used for a single function h, else the condition stands as is]*

- On page 94, f
_{Y}does not need that integral in the denominator*[correct, we already corrected for the truncation by subtracting 4.5 in the exponential]*

- On page 114, Problem 3.22, ω
_{i}is missing a factor of 1/n*[correct]*

- On page 218, in Example 6.24, P
_{00}=0*[indeed, our remark that P*_{xx}>0 should start with x=1. Note that this does not change the aperiodicity, though]

- On page 282, the
*log α*after the 2nd displayed equation should be*e*^{α}[correct, this was pointed out in a previous list of typos, but clearly not corrected in the latest printing!]

- On page 282, in the 5th displayed equation there are missing factors
*π(α’|b)/π(α*in rejection probability_{0}|b)*[actually, no, because, those terms being both proposals and priors, they cancel in the ratio. We could add a sentence to this effect to explain why, though.]*

- On page 634, the reference page for exponential distribution is mistakenly given as 99
*[wow, very thorough reading! There is an exponential distribution involved on page 99 but I agree this is not the relevant page…]*