Archive for MCMC algorithms

commentaries in financial econometrics

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , on April 27, 2016 by xi'an

My comment(arie)s on the moment approach to Bayesian inference by Ron Gallant have appeared, along with other comment(arie)s:

Invited Article
Reflections on the Probability Space Induced by Moment Conditions with
Implications for Bayesian Inference
A. Ronald Gallant . . . . . . . . . . . . . . . . . . . . . . . . . . . 229
Dante Amengual and Enrique Sentana .. . . . . . . . . . 248
John Geweke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .253
Jae-Young Kim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
Oliver Linton and Ruochen Wu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .261
Christian P. Robert . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
Christopher A. Sims . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
Wei Wei and Asger Lunde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  . . . . . . . . . .278
Author Response
A. Ronald Gallant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .284

formula (4) in Gallant's paperWhile commenting on commentaries is formally bound to induce an infinite loop [or l∞p], I remain puzzled by the main point of the paper, which is that setting a structural distribution on a moment function Z(x,θ) plus a prior p(θ) induces a distribution on the pair (x,θ) in a possibly weaker σ-algebra. (The two distributions may actually be incompatible.) Handling this framework requires checking that a posterior exists, which sounds rather unnatural (even though we also have to check properness of the posterior). And the meaning of such a posterior remains unclear, as for instance in this assertion that (4) above is a likelihood, when it does not define a density in x but on the object inside the exponential.

“…it is typically difficult to determine whether there exists a p(x|θ) such that the implied distribution of m(x,θ) is the one stated, and if not, what damage is done thereby” J. Geweke (p.254)

Continue reading

Rémi Bardenet’s seminar

Posted in Kids, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on April 7, 2016 by xi'an

Grand Palais from Esplanade des Invalides, Paris, Dec. 07, 2012Next week, Rémi Bardenet is giving a seminar in Paris, Thursday April 14, 2pm, in ENSAE [room 15] on MCMC methods for tall data. Unfortunately, I will miss this opportunity to discuss with Rémi as I will be heading to La Sapienza, Roma, for Clara Grazian‘s PhD defence the next day.  And on Monday afternoon, April 11, Nicolas Chopin will give a talk on quasi-Monte Carlo for sequential problems at Institut Henri Poincaré.

Statistical rethinking [book review]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , , , on April 6, 2016 by xi'an

Statistical Rethinking: A Bayesian Course with Examples in R and Stan is a new book by Richard McElreath that CRC Press sent me for review in CHANCE. While the book was already discussed on Andrew’s blog three months ago, and [rightly so!] enthusiastically recommended by Rasmus Bååth on Amazon, here are the reasons why I am quite impressed by Statistical Rethinking!

“Make no mistake: you will wreck Prague eventually.” (p.10)

While the book has a lot in common with Bayesian Data Analysis, from being in the same CRC series to adopting a pragmatic and weakly informative approach to Bayesian analysis, to supporting the use of STAN, it also nicely develops its own ecosystem and idiosyncrasies, with a noticeable Jaynesian bent. To start with, I like the highly personal style with clear attempts to make the concepts memorable for students by resorting to external concepts. The best example is the call to the myth of the golem in the first chapter, which McElreath uses as an warning for the use of statistical models (which almost are anagrams to golems!). Golems and models [and robots, another concept invented in Prague!] are man-made devices that strive to accomplish the goal set to them without heeding the consequences of their actions. This first chapter of Statistical Rethinking is setting the ground for the rest of the book and gets quite philosophical (albeit in a readable way!) as a result. In particular, there is a most coherent call against hypothesis testing, which by itself justifies the title of the book. Continue reading

perfect sampling, just perfect!

Posted in Books, Statistics, University life with tags , , , , , , , , on January 19, 2016 by xi'an

Great news! Mark Huber (whom I’ve know for many years, so this review may be not completely objective!) has just written a book on perfect simulation! I remember (and still share) the excitement of the MCMC community when the first perfect simulation papers of Propp and Wilson (1995) came up on the (now deceased) MCMC preprint server, as it seemed then the ideal (perfect!) answer to critics of the MCMC methodology, plugging MCMC algorithms into a generic algorithm that eliminating burnin, warmup, and convergence issues… It seemed both magical, with the simplest argument: “start at T=-∞ to reach stationarity at T=0”, and esoteric (“why forward fails while backward works?!”), requiring simple random walk examples (and a java app by Jeff Rosenthal) to understand the difference (between backward and forward), as well as Wilfrid Kendall’s kids’ coloured wood cubes and his layer of leaves falling on the ground and seen from below… These were exciting years, with MCMC still in its infancy, and no goal seemed too far away! Now that years have gone, and that the excitement has clearly died away, perfect sampling can be considered in a more sedate manner, with pros and cons well-understood. This is why Mark Huber’s book is coming at a perfect time if any! It covers the evolution of the perfect sampling techniques, from the early coupling from the past to the monotonous versions, to the coalescence principles, with applications to spatial processes, to the variations on nested sampling and their use in doubly intractable distributions, with forays into the (fabulous) Bernoulli factory problem (a surprise for me, as Bernoulli factories are connected with unbiasedness, not stationarity! Even though my only fieldwork [with Randal Douc] in such factories was addressing a way to turn MCMC into importance sampling. The key is in the notion of approximate densities, introduced in Section 2.6.). The book is quite thorough with the probabilistic foundations of the different principles, with even “a [tiny weeny] little bit of measure theory.

Any imperfection?! Rather, only a (short too short!) reflection on the limitations of perfect sampling, namely that it cannot cover the simulation of posterior distributions in the Bayesian processing of most statistical models. Which makes the quote

“Distributions where the label of a node only depends on immediate neighbors, and where there is a chance of being able to ignore the neighbors are the most easily handled by perfect simulation protocols (…) Statistical models in particular tend to fall into this category, as they often do not wish to restrict the outcome too severely, instead giving the data a chance to show where the model is incomplete or incorrect.” (p.223)

just surprising, given the very small percentage of statistical models which can be handled by perfect sampling. And the downsizing of perfect sampling related papers in the early 2000’s. Which also makes the final and short section on the future of perfect sampling somewhat restricted in its scope.

So, great indeed!, a close to perfect entry to a decade of work on perfect sampling. If you have not heard of the concept before, consider yourself lucky to be offered such a gentle guidance into it. If you have dabbled with perfect sampling before, reading the book will be like meeting old friends and hearing about their latest deeds. More formally, Mark Huber’s book should bring you a new perspective on the topic. (As for me, I had never thought of connecting perfect sampling with accept reject algorithms.)

precision in MCMC

Posted in Books, R, Statistics, University life with tags , , , , , , , , , on January 14, 2016 by xi'an

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While browsing Images des Mathématiques, I came across this article [in French] that studies the impact of round-off errors on number representations in a dynamical system and checked how much this was the case for MCMC algorithms like the slice sampler (recycling some R code from Monte Carlo Statistical Methods). By simply adding a few signif(…,dig=n) in the original R code. And letting the precision n vary.

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“…si on simule des trajectoires pendant des intervalles de temps très longs, trop longs par rapport à la précision numérique choisie, alors bien souvent, les résultats des simulations seront complètement différents de ce qui se passe en réalité…” Pierre-Antoine Guihéneuf

Rather unsurprisingly (!), using a small enough precision (like two digits on the first row) has a visible impact on the simulation of a truncated normal. Moving to three digits seems to be sufficient in this example… One thing this tiny experiment reminds me of is the lumpability property of Kemeny and Snell.  A restriction on Markov chains for aggregated (or discretised) versions to be ergodic or even Markov. Also, in 2000, Laird Breyer, Gareth Roberts and Jeff Rosenthal wrote a Statistics and Probability Letters paper on the impact of round-off errors on geometric ergodicity. However, I presume [maybe foolishly!] that the result stated in the original paper, namely that there exists an infinite number of precision digits for which the dynamical system degenerates into a small region of the space does not hold for MCMC. Maybe foolishly so because the above statement means that running a dynamical system for “too” long given the chosen precision kills the intended stationary properties of the system. Which I interpret as getting non-ergodic behaviour when exceeding the period of the uniform generator. More or less.

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approximating evidence with missing data

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , on December 23, 2015 by xi'an

University of Warwick, May 31 2010Panayiota Touloupou (Warwick), Naif Alzahrani, Peter Neal, Simon Spencer (Warwick) and Trevelyan McKinley arXived a paper yesterday on Model comparison with missing data using MCMC and importance sampling, where they proposed an importance sampling strategy based on an early MCMC run to approximate the marginal likelihood a.k.a. the evidence. Another instance of estimating a constant. It is thus similar to our Frontier paper with Jean-Michel, as well as to the recent Pima Indian survey of James and Nicolas. The authors give the difficulty to calibrate reversible jump MCMC as the starting point to their research. The importance sampler they use is the natural choice of a Gaussian or t distribution centred at some estimate of θ and with covariance matrix associated with Fisher’s information. Or derived from the warmup MCMC run. The comparison between the different approximations to the evidence are done first over longitudinal epidemiological models. Involving 11 parameters in the example processed therein. The competitors to the 9 versions of importance samplers investigated in the paper are the raw harmonic mean [rather than our HPD truncated version], Chib’s, path sampling and RJMCMC [which does not make much sense when comparing two models]. But neither bridge sampling, nor nested sampling. Without any surprise (!) harmonic means do not converge to the right value, but more surprisingly Chib’s method happens to be less accurate than most importance solutions studied therein. It may be due to the fact that Chib’s approximation requires three MCMC runs and hence is quite costly. The fact that the mixture (or defensive) importance sampling [with 5% weight on the prior] did best begs for a comparison with bridge sampling, no? The difficulty with such study is obviously that the results only apply in the setting of the simulation, hence that e.g. another mixture importance sampler or Chib’s solution would behave differently in another model. In particular, it is hard to judge of the impact of the dimensions of the parameter and of the missing data.

difference between Metropolis, Gibbs, importance, and rejection sampling

Posted in Books, Kids, Statistics with tags , , , , , on December 14, 2015 by xi'an

ofdawalLast week, while I was preparing my talk for the NIPS workshop, I spotted this fairly generic question on X validated. And decided to procrastinate by answering through generic comments on the pros and cons of each method. This is a challenging if probably empty question as it lacks a measure of evaluation for those different approaches.  And this is another reason why I replied, in that it relates to my pondering the a-statistical nature of simulation-based approximation methods. Also called probabilistic numerics, not statistical numerics, eh! It is indeed close to impossible to compare such approaches and others on a general basis. For instance, the comparative analysis greatly differs when dealing with a once-in-a-lifetime problem and with an everyday issue, e.g. when building a package for a sufficiently standard model. In the former case, a quick-and-dirty off-the-shelf solution is recommended, while in the latter, designing an efficient and fine-tuned approach makes sense. (The pros and cons I discussed in my X validated answer thus do not apply in most settings!) If anything, using several approaches, whenever possible, is the best advice to give. If not on the targeted problem, at least on a toy or simulated version, to check for performances of those different tools. But this brings back the issue of cost and time… An endless garden of forking paths, one would say [in another setting].


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