## recycling Gibbs auxiliaries [a reply]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , on January 3, 2017 by xi'an

[Here is a reply sent to me by Luca Martino, Victor Elvira, and Gustau Camp-Vallis, after my earlier comments on their paper.]

We provide our contribution to the discussion, reporting our experience with the application of Metropolis-within-Gibbs schemes. Since in literature there are miscellaneous opinions, we want to point out the following considerations:

– according to our experience, the use of M>1 steps of the Metropolis-Hastings (MH) method for drawing from each full-conditional (with or without recycling), decreases the MSE of the estimation (see code Ex1-Ex2 and related Figure 7(b) and Figures 8). If the corresponding full conditional is very concentrated, one possible solution is to applied an adaptive or automatic MH for drawing from this full-conditional (it can require the use of M internal steps; see references in Section 3.2).

– Fixing the number of evaluations of the posterior, the comparison between a longer Gibbs chain with a single step of MH and a shorter Gibbs chain with M>1 steps of MH per each full-conditional, is required. Generally, there is no clear winner. The better performance depends on different aspects: the specific scenario, if and adaptive MH is employed or not, if the recycling is applied or not (see Figure 10(a) and the corresponding code Ex2).

The previous considerations are supported/endorsed by several authors (see the references in Section 3.2). In order to highlight the number of controversial opinions about the MH-within-Gibbs implementation, we report a last observation:

– If it is possible to draw directly from the full-conditionals, of course this is the best scenario (this is our belief). Remarkably, as also reported in Chapter 1, page 393 of the book “Monte Carlo Statistical Methods”, C. Robert and Casella, 2004, some authors have found that a “bad” choice of the proposal function in the MH step (i.e., different from the full conditional, or a poor approximation of it) can improve the performance of the MH-within-Gibbs sampler. Namely, they assert that a more “precise” approximation of the full-conditional does not necessarily improve the overall performance. In our opinion, this is possibly due to the fact that the acceptance rate in the MH step (lower than 1) induces an “accidental” random scan of the components of the target pdf in the Gibbs sampler, which can improve the performance in some cases. In our work, for the simplicity, we only focus on the deterministic scan. However, a random scan could be also considered.

## puzzled by harmony [not!]

Posted in Books, Kids, Mountains, pictures, R, Running, Statistics, Travel with tags , , , , , on December 13, 2016 by xi'an

In answering yet another question on X validated about the numerical approximation of the marginal likelihood, I suggested using an harmonic mean estimate as a simple but worthless solution based on an MCMC posterior sample. This was on a toy example with a uniform prior on (0,π) and a “likelihood” equal to sin(θ) [really a toy problem!]. Simulating an MCMC chain by a random walk Metropolis-Hastings algorithm is straightforward, as is returning the harmonic mean of the sin(θ)’s.

f <- function(x){
if ((0<x)&(x<pi)){
return(sin(x))}else{
return(0)}}

n = 2000 #number of iterations
sigma = 0.5
x = runif(1,0,pi) #initial x value
chain = fx = f(x)
#generates an array of random x values from norm distribution
rands = rnorm(n,0, sigma)
#Metropolis - Hastings algorithm
for (i in 2:n){
can = x + rands[i]  #candidate for jump
fcan=f(can)
aprob = fcan/fx #acceptance probability
if (runif(1) < aprob){
x = can
fx = fcan}
chain=c(chain,fx)}
I = pi*length(chain)/sum(1/chain) #integral harmonic approximation

However, the outcome looks remarkably stable and close to the expected value 2/π, despite 1/sin(θ) having an infinite integral on (0,π). Meaning that the average of the 1/sin(θ)’s has no variance. Hence I wonder why this specific example does not lead to an unreliable output… But re-running the chain with a smaller scale σ starts producing values of sin(θ) regularly closer to zero, which leads to an estimate of I both farther away from 2 and much more variable. No miracle, in the end!

## ratio-of-uniforms [-1]

Posted in Books, pictures, R, Statistics, University life with tags , , , on December 12, 2016 by xi'an

Luca Martino pointed out to me my own and forgotten review of a 2012 paper of his, “On the Generalized Ratio of Uniforms as a Combination of Transformed Rejection and Extended Inverse of Density Sampling” that obviously discusses a generalised version of Kinderman and Monahan’s (1977) ratio-of-uniform method. And further points out the earlier 1991 paper by Jon Wakefield, Alan Gelfand and Adrian Smith that contains the general form I rediscovered a few posts ago… Called the GRoU in Martino et al.. While the generalisation in the massive arXiv document is in finding Φ such that the above region is bounded and can be explored by uniform sampling over a box.

Neither reference mentions using the cdf transform, though, which does guarantee a bounded ratio-of-uniform set in u. (An apparent contradiction with Martino et al.  statement (34), unless I am confused. Maybe due to using Φ⁻¹ instead of Φ?) But I still wonder at the usefulness of my derivations those past weeks!

## Sobol’s Monte Carlo

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , on December 10, 2016 by xi'an

The name of Ilya Sobol is familiar to researchers in quasi-Monte Carlo methods for his Sobol’s sequences. I was thus surprised to find in my office a small book entitled The Monte Carlo Method by this author, which is a translation of his 1968 book in Russian. I have no idea how it reached my office and I went to check with the library of Paris-Dauphine around the corner [of my corridor] whether it had been lost: apparently, the library got rid of it among a collection of old books… Now, having read through this 67 pages book (or booklet as Sobol puts it) makes me somewhat agree with the librarians, in that there is nothing of major relevance in this short introduction. It is quite interesting to go through the book and see the basics of simulation principles and Monte Carlo techniques unfolding, from the inverse cdf principle [established by a rather convoluted proof] to importance sampling, but the amount of information is about equivalent to the Wikipedia entry on the topic. From an historical perspective, it is also captivating to see the efforts to connect physical random generators (such as those based on vacuum tube noise) to shift-register pseudo-random generators created by Sobol in 1958. On a Soviet Strela computer.

While Googling the title of that book could not provide any connection, I found out that a 1994 version had been published under the title of A Primer for the Monte Carlo Method, which is mostly the same as my version, except for a few additional sections on pseudo-random generation, from the congruential method (with a FORTRAN code) to the accept-reject method being then called von Neumann’s instead of Neyman’s, to the notion of constructive dimension of a simulation technique, which amounts to demarginalisation, to quasi-Monte Carlo [for three pages]. A funny side note is that the author notes in the preface that the first translation [now in my office] was published without his permission!

## recycling Gibbs auxiliaries

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , on December 6, 2016 by xi'an

Luca Martino, Victor Elvira and Gustau Camps-Valls have arXived a paper on recycling for Gibbs sampling. The argument therein is to take advantage of all simulations induced by MCMC simulation for one full conditional, towards improving estimation if not convergence. The context is thus one when Metropolis-within-Gibbs operates, with several (M) iterations of the corresponding Metropolis being run instead of only one (which is still valid from a theoretical perspective). While there are arguments in augmenting those iterations, as recalled in the paper, I am not a big fan of running a fixed number of M of iterations as this does not approximate better the simulation from the exact full conditional and even if this approximation was perfect, the goal remains simulating from the joint distribution. As such, multiplying the number of Metropolis iterations does not necessarily impact the convergence rate, only brings it closer to the standard Gibbs rate. Moreover, the improvement does varies with the chosen component, meaning that the different full conditionals have different characteristics that produce various levels of variance reduction:

• if the targeted expectation only depends on one component of the Markov chain, multiplying the number of simulations for the other components has no clear impact, except in increasing time;
• if the corresponding full conditional is very concentrated, repeating simulations should produce quasi-repetitions, and no gain.

The only advantage in computing time that I can see at this stage is when constructing the MCMC sampler for the full proposal is much more costly than repeating MCMC iterations, which are then almost free and contribute to the reduction of the variance of the estimator.

This analysis of MCMC-withing-Gibbs strategies reminds me of a recent X validated question, which was about the proper degree of splitting simulations from a marginal and from a corresponding conditional in the chain rule, the optimal balance being in my opinion dependent on the relative variances of the conditional expectations.

A last point is that recycling in the context of simulation and Monte Carlo methodology makes me immediately think of Rao-Blackwellisation, which is surprisingly absent from the current paperRao-Blackwellisation was introduced in the MCMC literature and to the MCMC community in the first papers of Alan Gelfand and Adrian Smith, in 1990. While this is not always producing a major gain in Monte Carlo variability, it remains a generic way of recycling auxiliary variables as shown, e.g., in the recycling paper we wrote with George Casella in 1996, one of my favourite papers.

## simulation by hand

Posted in Books, Kids, pictures, Statistics, Travel with tags , , , , , , , on November 28, 2016 by xi'an

A rather weird question on X validated this week was about devising a manual way to simulate (a few) normal variates. By manual I presume the author of the question means without resorting to a computer or any other business machine. Now, I do not know of any real phenomenon that is exactly and provably Normal. As analysed in a great philosophy of science paper by Aidan Lyon, the standard explanations for a real phenomenon to be Normal are almost invariably false, even those invoking the Central Limit Theorem. Hence I cannot think of a mechanical device that would directly return Normal generations from a Normal distribution with known parameters. However, since it is possible to simulate by hand Uniform U(0,1) variates [up to a given precision] using a chronometre or a wheel, calls to versions of the Box-Müller algorithm that do not rely on logarithmic or trigonometric functions are feasible, for instance by generating two Exponential variates, x and y, until 2y>(1-x)², x being the output. And generating Exponential variates is easy provided a radioactive material with known half-life is available, along with a Geiger counter. Or, if not, by calling von Neumann’s exponential generator. As detailed in Devroye’s simulation book.

After proposing this solution, I received a comment from the author of the question towards a simpler solution based, e.g., on the Central Limit Theorem. Presumably for simple iid random variables such as coin tosses or dice experiments. While I used the CLT for simulating Normal variables in my very early days [just after programming on punched cards!], I do not think this is a very good or efficient method, as the tails grow very slowly to normality. By comparison, using the same amount of coin tosses to create a sufficient number of binary digits of a Uniform variate produces a computer-precision exact Uniform variate, which can be exploited in Box-Müller-like algorithms to return exact Normal variates… Even by hand if necessary. [For some reason, this question attracted a lot of traffic and an encyclopaedic answer on X validated, despite being borderline to the point of being proposed for closure.]

## Monty Python generator

Posted in Books, Kids, pictures, R, Statistics, University life with tags , , , , , , , on November 23, 2016 by xi'an

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 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

$q(x)=(a-1/3)(1 + x/\sqrt{16a})^3$

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.