## ABCπ

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , on May 17, 2017 by xi'an

Ritabrata Dutta, Marcel Schöengens, Jukka-Pekka Onnela, and Antonietta Mira recently put a new ABC software on-line, called ABCpy for ABC with Python. The software aims at  an automated parallelisation of ABC runs, requiring only code to generate from the (generative) model and the choice of summary statistics and of associated distance. Alternatively an approximate likelihood (as in synthetic likelihood) can be used. The tolerance ε is chosen as a percentile of the prior predictive distribution on the distance. The versions of ABC found in ABCpy are

1. Population Monte Carlo for ABC (PMCABC);
2. sequential Monte Carlo ABC (ABC-SMC);
3. replenishment Sequential Monte Carlo ABC (RSMC-ABC);
4. adaptive Population Monte Carlo ABC (APMCABC);
5. ABC with subset simulation (ABCsubsim); and
6. simulated annealing ABC (SABC)

Anto mentioned ABCpy to me while in Harvard last week and I have not tested the program (my only brush with Python being the occasional call to latex2wp for SeriesB’log). And obviously, writing a blog about Monte (Carlo and) Python makes a link to the Monty Pythons irresistible:

## 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.

## MCMskv #3 [town with a view]

Posted in Statistics with tags , , , , , , , , , , , , , on January 8, 2016 by xi'an

Third day at MCMskv, where I took advantage of the gap left by the elimination of the Tweedie Race [second time in a row!] to complete and submit our mixture paper. Despite the nice weather. The rest of the day was quite busy with David Dunson giving a plenary talk on various approaches to approximate MCMC solutions, with a broad overview of the potential methods and of the need for better solutions. (On a personal basis, great line from David: “five minutes or four minutes?”. It almost beat David’s question on the previous day, about the weight of a finch that sounded suspiciously close to the question about the air-speed velocity of an unladen swallow. I was quite surprised the speaker did not reply with the Arthurian “An African or an European finch?”) In particular, I appreciated the notion that some problems were calling for a reduction in the number of parameters, rather than the number of observations. At which point I wrote down “multiscale approximations required” in my black pad,  a requirement David made a few minutes later. (The talk conditions were also much better than during Michael’s talk, in that the man standing between the screen and myself was David rather than the cameraman! Joke apart, it did not really prevent me from reading them, except for most of the jokes in small prints!)

The first session of the morning involved a talk by Marc Suchard, who used continued fractions to find a closed form likelihood for the SIR epidemiology model (I love continued fractions!), and a talk by Donatello Telesca who studied non-local priors to build a regression tree. While I am somewhat skeptical about non-local testing priors, I found this approach to the construction of a tree quite interesting! In the afternoon, I obviously went to the intractable likelihood session, with talks by Chris Oates on a control variate method for doubly intractable models, Brenda Vo on mixing sequential ABC with Bayesian bootstrap, and Gael Martin on our consistency paper. I was not aware of the Bayesian bootstrap proposal and need to read through the paper, as I fail to see the appeal of the bootstrap part! I later attended a session on exact Monte Carlo methods that was pleasantly homogeneous. With talks by Paul Jenkins (Warwick) on the exact simulation of the Wright-Fisher diffusion, Anthony Lee (Warwick) on designing perfect samplers for chains with atoms, Chang-han Rhee and Sebastian Vollmer on extensions of the Glynn-Rhee debiasing technique I previously discussed on the blog. (Once again, I regretted having to make a choice between the parallel sessions!)

The poster session (after a quick home-made pasta dish with an exceptional Valpolicella!) was almost universally great and with just the right number of posters to go around all of them in the allotted time. With in particular the Breaking News! posters of Giacomo Zanella (Warwick), Beka Steorts and Alexander Terenin. A high quality session that made me regret not touring the previous one due to my own poster presentation.

## The synoptic problem and statistics [book review]

Posted in Books, R, Statistics, University life, Wines with tags , , , , , , , , , , , , on March 20, 2015 by xi'an

A book that came to me for review in CHANCE and that came completely unannounced is Andris Abakuks’ The Synoptic Problem and Statistics.  “Unannounced” in that I had not heard so far of the synoptic problem. This problem is one of ordering and connecting the gospels in the New Testament, more precisely the “synoptic” gospels attributed to Mark, Matthew and Luke, since the fourth canonical gospel of John is considered by experts to be posterior to those three. By considering overlaps between those texts, some statistical inference can be conducted and the book covers (some of?) those statistical analyses for different orderings of ancestry in authorship. My overall reaction after a quick perusal of the book over breakfast (sharing bread and fish, of course!) was to wonder why there was no mention made of a more global if potentially impossible approach via a phylogeny tree considering the three (or more) gospels as current observations and tracing their unknown ancestry back just as in population genetics. Not because ABC could then be brought into the picture. Rather because it sounds to me (and to my complete lack of expertise in this field!) more realistic to postulate that those gospels were not written by a single person. Or at a single period in time. But rather that they evolve like genetic mutations across copies and transmission until they got a sort of official status.

“Given the notorious intractability of the synoptic problem and the number of different models that are still being advocated, none of them without its deficiencies in explaining the relationships between the synoptic gospels, it should not be surprising that we are unable to come up with more definitive conclusions.” (p.181)

The book by Abakuks goes instead through several modelling directions, from logistic regression using variable length Markov chains [to predict agreement between two of the three texts by regressing on earlier agreement] to hidden Markov models [representing, e.g., Matthew’s use of Mark], to various independence tests on contingency tables, sometimes bringing into the model an extra source denoted by Q. Including some R code for hidden Markov models. Once again, from my outsider viewpoint, this fragmented approach to the problem sounds problematic and inconclusive. And rather verbose in extensive discussions of descriptive statistics. Not that I was expecting a sudden Monty Python-like ray of light and booming voice to disclose the truth! Or that I crave for more p-values (some may be found hiding within the book). But I still wonder about the phylogeny… Especially since phylogenies are used in text authentication as pointed out to me by Robin Ryder for Chauncer’s Canterbury Tales.