Xian's Og

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Updated: 14 hours 36 min ago

PMC for combinatoric spaces

Sun, 2014-07-27 18:14

I received this interesting [edited] email from Xiannian Fan at CUNY:

I am trying to use PMC to solve Bayesian network structure learning problem (which is in a combinatorial space, not continuous space).

In PMC, the proposal distributions qi,t can be very flexible, even specific to each iteration and each instance. My problem occurs due to the combinatorial space.

For importance sampling, the requirement for proposal distribution, q, is:

support (p) ⊂ support (q)             (*)

For PMC, what is the support of the proposal distribution in iteration t? is it

support (p) ⊂ U support(qi,t)    (**)

or does (*) apply to every qi,t?

For continuous problem, this is not a big issue. We can use random walk of Normal distribution to do local move satisfying (*). But for combination search, local moving only result in finite states choice, just not satisfying (*). For example for a permutation (1,3,2,4), random swap has only choose(4,2)=6 neighbor states.

Fairly interesting question about population Monte Carlo (PMC), a sequential version of importance sampling we work on with French colleagues in the early 2000′s.  (The name population Monte Carlo comes from Iba, 2000.)  While MCMC samplers do not have to cover the whole support of p at each iteration, it is much harder for importance samplers as their core justification is to provide an unbiased estimator to for all integrals of interest. Thus, when using the PMC estimate,

1/n ∑i,t {p(xi,t)/qi,t(xi,t)}h(qi,t),  xi,t~qi,t(x)

this estimator is only unbiased when the supports of the qi,t “s are all containing the support of p. The only other cases I can think of are

  1. associating the qi,t “s with a partition Si,t of the support of p and using instead

    ∑i,t {p(xi,t)/qi,t(xi,t)}h(qi,t), xi,t~qi,t(x)

  2. resorting to AMIS under the assumption (**) and using instead

    1/n ∑i,t {p(xi,t)/∑j,t qj,t(xi,t)}h(qi,t), xi,t~qi,t(x)

but I am open to further suggestions!

Filed under: Statistics, University life Tagged: AMIS, CUNY, importance sampling, Monte Carlo Statistical Methods, PMC, population Monte Carlo, simulation, unbiasedness
Categories: Bayesian Bloggers

off to Bangalore [#2]

Sun, 2014-07-27 08:18

While I was trying to find a proper window to take a picture of the mountains of Eastern Turkey, an Air France flight attendant suggested me to try the view from the pilots’ cockpit! I thought she was joking but, after putting a request to the captain, she came to walk me there and I had a fantastic five minutes with the pilots, chatting and taking unconstrained views of the region of Van, as we were nearing Turkey. I was actually most surprised at the very possibility of entering the cockpit as I thought it was now completely barred to passengers. Thanks then to the Air France crew that welcomed me there!

Filed under: Mountains, pictures, Travel Tagged: Air France, cockpit, Mount Süphan, Turkey, Van Lake
Categories: Bayesian Bloggers

Ulam’s grave [STAN post]

Sat, 2014-07-26 18:17

Since Stan Ulam is buried in Cimetière du Montparnasse, next to CREST, Andrew and I paid his grave a visit on a sunny July afternoon. Among elaborate funeral constructions, the Aron family tomb is sober and hidden behind funeral houses. It came as a surprise to me to discover that Ulam had links with France to the point of him and his wife being buried in Ulam’s wife family vault. Since we were there, we took a short stroll to see Henri Poincaré’s tomb in the Poincaré-Boutroux vault (missing Henri’s brother, the French president Raymond Poincaré). It came as a surprise that someone had left a folder with the cover of 17 equations that changed the World on top of the tomb). Even though the book covers Poincaré’s work on the three body problem as part of Newton’s formula. There were other mathematicians in this cemetery, but this was enough necrophiliac tourism for one day.

Filed under: Books, Kids, pictures, Travel, University life Tagged: 17 equations That Changed the World, cemetary, Cimetière du Montparnasse, France, Henri Poincaré, Paris, STAN, Stanislas Ulam
Categories: Bayesian Bloggers

off to Bangalore

Sat, 2014-07-26 08:18

I am off to Bangalore for a few days, taking part in an Indo-French workshop on statistics and mathematical biology run by the Indo-French Centre for Applied Mathematics (IFCAM).

Filed under: Statistics, Travel, University life Tagged: Bangalore, IFCAM, India, workshop
Categories: Bayesian Bloggers

art brut

Fri, 2014-07-25 18:14
Categories: Bayesian Bloggers

a statistical test for nested sampling

Thu, 2014-07-24 18:14

A new arXival on nested sampling: “A statistical test for nested sampling algorithms” by Johannes Buchner. The point of the test is to check if versions of the nested sampling algorithm that fail to guarantee increased likelihood (or nesting) at each step are not missing parts of the posterior mass. and hence producing biased evidence approximations. This applies to MultiNest for instance. This version of nest sampling evaluates the above-threshold region by drawing hyper-balls around the remaining points. A solution which is known to fail in one specific but meaningful case. Buchner’s  arXived paper proposes an hyper-pyramid distribution for which the volume of any likelihood constrained set is known. Hence allowing for a distribution test like Kolmogorov-Smirnov. Confirming the findings of Beaujean and Caldwell (2013). The author then proposes an alternative to MultiNest that is more robust but also much more costly as it computes distances between all pairs of bootstrapped samples. This solution passes the so-called “shrinkage test”, but it is orders of magnitude less efficient than MultiNest. And also simply shows that its coverage is fine for a specific target rather than all possible targets. I wonder if a solution to the problem is at all possible given that evaluating a support or a convex hull is a complex problem which complexity explodes with the dimension.

Filed under: Books, Statistics, University life Tagged: complexity, evidence, Kolmogorov-Smirnov distance, Multinest, nested sampling, shrinkage test
Categories: Bayesian Bloggers

ABC in Sydney [guest post #2]

Wed, 2014-07-23 18:14

[Here is a second guest post on the ABC in Sydney workshop, written by Chris Drovandi]

First up Dennis Prangle presented his recent work on “Lazy ABC”, which can speed up ABC by potentially abandoning model simulations early that do not look promising. Dennis introduces a continuation probability to ensure that the target distribution of the approach is still the ABC target of interest. In effect, the ABC likelihood is estimated to be 0 if early stopping is performed otherwise the usual ABC likelihood is inflated by dividing by the continuation probability, ensuring an unbiased estimator of the ABC likelihood. The drawback is that the ESS (Dennis uses importance sampling) of the lazy approach will likely be less than usual ABC for a fixed number of simulations; but this should be offset by the reduction in time required to perform said simulations. Dennis also presented some theoretical work for optimally tuning the method, which I need more time to digest.
This was followed by my talk on Bayesian indirect inference methods that use a parametric auxiliary model (a slightly older version here). This paper has just been accepted by Statistical Science.
Morning tea was followed by my PhD student, Brenda Vo, who presented an interesting application of ABC to cell spreading experiments. Here an estimate of the diameter of the cell population was used as a summary statistic. It was noted after Brenda’s talk that this application might be a good candidate for Dennis’ Lazy ABC idea. This talk was followed by a much more theoretical presentation by Pierre del Moral on how particle filter methodologies can be adapted to the ABC setting and also a general framework for particle methods.
Following lunch, Guilherme Rodrigues presented a hierarchical Gaussian Process model for kernel density estimation in the presence of different subgroups. Unfortunately my (lack of) knowledge on non-parametric methods prevents me from making any further comment except that the model looked very interesting and ABC seemed a good candidate for calibrating the model. I look forward to the paper appearing on-line.
The next presentation was by Gael Martin who spoke about her research on using ABC for estimation of complex state space models. This was probably my favourite talk of the day, and not only because it is very close to my research interests. Here the score of the Euler discretised approximation of the generative model was used as summary statistics for ABC. From what I could gather, it was demonstrated that the ABC posterior based on the score or the MLE of the auxiliary model were the same in the limit as ε 0 (unless I have mis-interpreted). This is a very useful result in itself; using the score to avoid an optimisation required for the MLE can save a lot of computation. The improved approximations of the proposed approach compared with the results that use the likelihood of the Euler discretisation were quite promising. I am certainly looking forward to this paper coming out.
Matt Moores drew the short straw and had the final presentation on the Friday afternoon. Matt spoke about this paper (an older version is available here), of which I am now a co-author. Matt’s idea is that doing some pre-simulations across the prior space and determining a mapping between the parameter of interest and the mean and variance of the summary statistic can significantly speed up ABC for the Potts model, and potentially other ABC applications. The results of the pre-computation step are used in the main ABC algorithm, which no longer requires simulation of pseudo-data but rather a summary statistic can be simulated from the fitted auxiliary model in the pre-processing step. Whilst this approach does introduce a couple more layers of approximation, the gain in computation time was up to two orders of magnitude. The talks by Matt, Gael and myself gave a real indirect inference flavour to this year’s ABC in…

Filed under: pictures, Statistics, University life Tagged: abc-in-sydney, Australia, Chris Drovandi, Sydney
Categories: Bayesian Bloggers

Cancún, ISBA 2014 [day #3]

Tue, 2014-07-22 18:14

…already Thursday, our [early] departure day!, with an nth (!) non-parametric session that saw [the newly elected ISBA Fellow!] Judith Rousseau present an ongoing work with Chris Holmes on the convergence or non-convergence conditions for a Bayes factor of a non-parametric hypothesis against another non-parametric. I wondered at the applicability of this test as the selection criterion in ABC settings, even though having an iid sample to start with is a rather strong requirement.

Switching between a scalable computation session with Alex Beskos, who talked about adaptive Langevin algorithms for differential equations, and a non-local prior session, with David Rossell presenting a smoother way to handle point masses in order to accommodate frequentist coverage. Something we definitely need to discuss the next time I am in Warwick! Although this made me alas miss both the first talk of the non-local session by Shane Jensen  the final talk of the scalable session by Doug Vandewrken where I happened to be quoted (!) for my warning about discretising Markov chains into non-Markov processes. In the 1998 JASA paper with Chantal Guihenneuc.

After a farewell meal of ceviche with friends in the sweltering humidity of a local restaurant, I attended [the newly elected ISBA Fellow!] Maria Vanucci’s talk on her deeply involved modelling of fMRI. The last talk before the airport shuttle was François Caron’s description of a joint work with Emily Fox on a sparser modelling of networks, along with an auxiliary variable approach that allowed for parallelisation of a Gibbs sampler. François mentioned an earlier alternative found in machine learning where all components of a vector are updated simultaneously conditional on the previous avatar of the other components, e.g. simulating (x’,y’) from π(x’|y) π(y’|x) which does not produce a convergent Markov chain. At least not convergent to the right stationary. However, running a quick [in-flight] check on a 2-d normal target did not show any divergent feature, when compared with the regular Gibbs sampler. I thus wonder at what can be said about the resulting target or which conditions are need for divergence. A few scribbles later, I realised that the 2-d case was the exception, namely that the stationary distribution of the chain is the product of the marginal. However, running a 3-d example with an auto-exponential distribution in the taxi back home, I still could not spot a difference in the outcome.

Filed under: pictures, Statistics, Travel, University life Tagged: Cancún, ISBA, Langevin MCMC algorithm, MCMC algorithms, non-local priors, University of Warwick
Categories: Bayesian Bloggers

Off from Cancun [los scientificos Maya]

Mon, 2014-07-21 18:14

The flight back from ISBA 2014 was not as smooth as the flight in: it took one hour for the shuttle to take us to the airport thanks to a driver posing as a touristic guide [who needs a guide when going home?!] and droning on and on about Cancún and the Maya heritage [as far as I could guess from his Spanish]. Learning at the airport that out flight to Mexico City was delayed, then too delayed for us to make the connection, with no hotel room available there, then suggesting to the desk personal every possible European city to learn the flight had left or was about to leave, missing London by an hair, thanks to our droning friend on the scientific Mayas, and eventually being bused to the hotel airport, too far from the last poster session we could have attended!, and leaving early the next morning to Atlanta and then Paris. Which means we could have stayed for most of the remaining sessions and been back home at about the same time…

Filed under: pictures, Statistics, Travel, University life Tagged: Aero Mexico, Cancún, flight, ISBA 2014, Maya, Mexico, poster session
Categories: Bayesian Bloggers

Gallo Zinfandel

Sun, 2014-07-20 14:20
Categories: Bayesian Bloggers

do and [mostly] don’t…

Sat, 2014-07-19 18:14

Rather than staying in one of the conference hotels, I followed my habit of renting a flat by finding a nice studio in Cancún via airbnb. Fine except for having no internet connection. (The rental description mentioned “Wifi in the lobby”, which I stupidly interpreted as “lobby of the appartment”, but actually meant “lobby of the condominium building”… Could as well have been “lobby of the airport”.) The condo owner sent us a list of “don’t” a few days ago, some of which are just plain funny (or tell of past disasters!):

- don’t drink heavily
- don’t party or make noise
- don’t host visitors, day or night
- don’t bang the front door or leave the balcony door open when opening the front door
- don’t put cans or bottles on top of the glass cooktop
- don’t cook elaborate meals
- don’t try to fit an entire chicken in the oven
- don’t spill oil or wine on the kitchentop
- don’t cut food directly on the kitchentop
- don’t eat or drink while in bed
- avoid frying, curry, and bacon
- shop for groceries only one day at a time
- hot water may or may not be available
- elevator may or may not be available
- don’t bring sand back in the condo

Filed under: pictures, Travel Tagged: beach, Cancún, condo, flat, Mexico, rental
Categories: Bayesian Bloggers

Cancun, ISBA 2014 [½ day #2]

Fri, 2014-07-18 18:14

Half-day #2 indeed at ISBA 2014, as the Wednesday afternoon kept to the Valencia tradition of free time, and potential cultural excursions, so there were only talks in the morning. And still the core poster session at (late) night. In which my student Kaniav Kamari presented a poster on a current project we are running with Kerrie Mengersen and Judith Rousseau on the replacement of the standard Bayesian testing setting with a mixture representation. Being half-asleep by the time the session started, I did not stay long enough to collect data on the reactions to this proposal, but the paper should be arXived pretty soon. And Kate Lee gave a poster on our importance sampler for evidence approximation in mixtures (soon to be revised!). There was also an interesting poster about reparameterisation towards higher efficiency of MCMC algorithms, intersecting with my long-going interest in the matter, although I cannot find a mention of it in the abstracts. And I had a nice talk with Eduardo Gutierrez-Pena about infering on credible intervals through loss functions. There were also a couple of appealing posters on g-priors. Except I was sleepwalking by the time I spotted them… (My conference sleeping pattern does not work that well for ISBA meetings! Thankfully, both next editions will be in Europe.)

Great talk by Steve McEachern that linked to our ABC work on Bayesian model choice with insufficient statistics, arguing towards robustification of Bayesian inference by only using summary statistics. Despite this being “against the hubris of Bayes”… Obviously, the talk just gave a flavour of Steve’s perspective on that topic and I hope I can read more to see how we agree (or not!) on this notion of using insufficient summaries to conduct inference rather than trying to model “the whole world”, given the mistrust we must preserve about models and likelihoods. And another great talk by Ioanna Manolopoulou on another of my pet topics, capture-recapture, although she phrased it as a partly identified model (as in Kline’s talk yesterday). This related with capture-recapture in that when estimating a capture-recapture model with covariates, sampling and inference are biased as well. I appreciated particularly the use of BART to analyse the bias in the modelling. And the talk provided a nice counterpoint to the rather pessimistic approach of Kline’s.

Terrific plenary sessions as well, from Wilke’s spatio-temporal models (in the spirit of his superb book with Noel Cressie) to Igor Prunster’s great entry on Gibbs process priors. With the highly significant conclusion that those processes are best suited for (in the sense that they are only consistent for) discrete support distributions. Alternatives are to be used for continuous support distributions, the special case of a Dirichlet prior constituting a sort of unique counter-example. Quite an inspiring talk (even though I had a few micro-naps throughout it!).

I shared my afternoon free time between discussing the next O’Bayes meeting (2015 is getting very close!) with friends from the Objective Bayes section, getting a quick look at the Museo Maya de Cancún (terrific building!), and getting some work done (thanks to the lack of wireless…)

Filed under: pictures, Running, Statistics, Travel, University life Tagged: ABC, Bayesian tests, beach, Cancún, g-priors, ISBA 2014, Maya, Mexico, mixture estimation, O-Bayes 2015, posters, sunrise, Valencia conferences
Categories: Bayesian Bloggers

Cancún, ISBA 2014 [day #1]

Thu, 2014-07-17 18:14

The first full day of talks at ISBA 2014, Cancún, was full of goodies, from the three early talks on specifically developed software, including one by Daniel Lee on STAN that completed the one given by Bob Carpenter a few weeks ago in Paris (which gives me the opportunity to advertise STAN tee-shirts!). To the poster session (which just started a wee bit late for my conference sleep pattern!). Sylvia Richardson gave an impressive lecture full of information on Bayesian genomics. I also enjoyed very much two sessions with young Bayesian statisticians, one on Bayesian econometrics and the other one more diverse and sponsored by ISBA. Overall, and this also applies to the programme of the following days, I found that the proportion of non-parametric talks was quite high this year, possibly signalling a switch in the community and the interest of Bayesians. And conversely very few talks on computing related issues. (With most scheduled after my early departure…)

In the first of those sessions, Brendan Kline talked about partially identified parameters, a topic quite close to my interests, although I did not buy the overall modelling adopted in the analysis. For instance, Brendan Kline presented the example of a parameter θ that is the expectation of a random variable Y which is indirectly observed through x <Y< x̅ . While he maintained that inference should be restricted to an interval around θ and that using a prior on θ was doomed to fail (and against econometrics culture), I would have prefered to see this example as a missing data one, with both x and x̅ containing information about θ. And somewhat object to the argument against the prior as it would equally apply to any prior modelling. Although unrelated in the themes, Angela Bitto presented a work on the impact of different prior modellings on the estimation of time-varying parameters in time-series models. À la Harrison and West 1994 Discriminating between good and poor shrinkage in a way I could not spot. Unless it was based on the data fit (horror!). And a third talk of interest by Andriy Norets that (very loosely) related to Angela’s talk by presenting a framework to modify credible sets towards frequentist properties: one example was the credible interval on a positive normal mean that led to a frequency-valid confidence interval with a modified prior. This reminded me very much of the shrinkage confidence intervals of the James-Stein era.

Filed under: pictures, Statistics, Travel, University life Tagged: Bayesian statistics, Cancún, econometrics, genomics, ISBA 2004, Mexico, poster, shrinkage estimation

Categories: Bayesian Bloggers

ABC in Sydney [guest post]

Thu, 2014-07-17 18:14

[Scott Sisson sent me this summary of the ABC in Sydney meeting that took place two weeks ago.]

Following on from ABC in Paris (2009), ABC in London (2011) and ABC in Rome (2013), the fourth instalment of the international workshops in Approximate Bayesian Computation (ABC) was held at UNSW in Sydney on 3rd-4th July 2014. The first antipodean workshop was held as a satellite to the huge (>550 registrations) IMS-ASC-2014 International Conference, also held in Sydney the following week.

ABC in Sydney was created in two parts. The first, on the Thursday, was held as an “introduction to ABC” for people who were interested to find out more about the subject, but who had not particularly been exposed to the area before. Rather than have a single brave individual give the introductory course over several hours, the expository presentation was “crowdsourced” from several experienced researchers in the field, with each being given 30 minutes to present on a particular aspect of ABC. In this way, Matthew Moores (QUT), Dennis Prangle (Reading), Chris Drovandi (QUT), Zach Aandahl (UNSW) and Scott Sisson (UNSW) covered the ABC basics over the course of 6 presentations and 3 hours.

The second part of the workshop, on Friday, was the more usual collection of research oriented talks. In the morning session, Dennis Prangle spoke about “lazy ABC,” a method of stopping the generation of computationally demanding dataset simulations early, and Chris Drovandi discussed theoretical and practical aspects of Bayesian indirect inference. This was followed by Brenda Nho Vo (QUT) presenting an application of ABC in stochastic cell spreading models, and by Pierre Del Moral (UNSW) who demonstrated many theoretical aspects of ABC in interacting particle systems. After lunch Guilherme Rodrigues (UNSW) proposed using ABC for Gaussian process density estimation (and introduced the infinite-dimensional functional regression adjustment), and Gael Martin (Monash) spoke on the issues involved in applying ABC to state space models. The final talk of the day was given by Matthew Moores who discussed how online ABC dataset generation could be circumvented by pre-computation for particular classes of models.

In all, over 100 people registered for and attended the workshop, making it an outstanding success. Of course, this was helped by the association with the following large conference, and the pricing scheme — completely free! — following the tradition of the previous workshops. Morning and afternoon teas, described as “the best workshop food ever!” by several attendees, was paid for by the workshop sponsors: the Bayesian Section of the Statistical Society of Australia, and the ARC Centre of Excellence in Mathematical and Statistical Frontiers.

Here’s looking forward to the next workshop in the series!

Filed under: pictures, Statistics, University life Tagged: abc-in-sydney, Australia, Scott Sisson, Sydney
Categories: Bayesian Bloggers

Cancún, ISBA 2014 [day #0]

Wed, 2014-07-16 18:38

Day zero at ISBA 2014! The relentless heat outside (making running an ordeal, even at 5:30am…) made the (air-conditioned) conference centre the more attractive. Jean-Michel Marin and I had a great morning teaching our ABC short course and we do hope the ABC class audience had one as well. Teaching in pair is much more enjoyable than single as we can interact with one another as well as the audience. And realising unsuspected difficulties with the material is much easier this way, as the (mostly) passive instructor can spot the class’ reactions. This reminded me of the course we taught together in Oulu, northern Finland, in 2004 and that ended as the Bayesian Core. We did not cover the entire material we have prepared for this short course, but I think the pace was the right one. (Just tell me otherwise if you were there!) This was also the only time I had given a course wearing sunglasses, thanks to yesterday’s incident!

Waiting for a Spanish speaking friend to kindly drive with me downtown Cancún to check whether or not an optician could make me new prescription glasses, I attended Jim Berger’s foundational lecture on frequentist properties of Bayesian procedures but could only listen as the slides were impossible for me to read, with or without glasses. The partial overlap with the Varanasi lecture helped. I alas had to skip both Gareth Roberts’ and Sylvia Früwirth-Schnatter’s lectures, apologies to both of them!, but the reward was to get a new pair of prescription glasses within a few hours. Perfectly suited to my vision! And to get back just in time to read slides during Peter Müller’s lecture from the back row! Thanks to my friend Sophie for her negotiating skills! Actually, I am still amazed at getting glasses that quickly, given the time it would have taken in, e.g., France. All set for another 15 years with the same pair?! Only if I do not go swimming with them in anything but a quiet swimming pool!

The starting dinner happened to coincide with the (second) ISBA Fellow Award ceremony. Jim acted as the grand master of ceremony and he did great to add life and side stories to the written nominations for each and everyone of the new Fellows. The Fellowships honoured Bayesian statisticians who had contributed to the field as researchers and to the society since its creation. I thus feel very honoured (and absolutely undeserving) to be included in this prestigious list, along with many friends.  (But would have loved to see two more former ISBA presidents included, esp. for their massive contribution to Bayesian theory and methodology…) And also glad to wear regular glasses instead of my morning sunglasses.

[My Internet connection during the meeting being abysmally poor, the posts will appear with some major delay! In particular, I cannot include new pictures at times I get a connection... Hence a picture of northern Finland instead of Cancún at the top of this post!]

Filed under: Statistics, Travel, University life Tagged: ABC, Cancún, Caribean sea, ISBA, Jim Berger, Mexico, short course, sunglasses, Valencia conferences
Categories: Bayesian Bloggers

another R new trick [new for me!]

Tue, 2014-07-15 18:14

While working with Andrew and a student from Dauphine on importance sampling, we wanted to assess the distribution of the resulting sample via the Kolmogorov-Smirnov measure

where F is the target.  This distance (times √n) has an asymptotic distribution that does not depend on n, called the Kolmogorov distribution. After searching for a little while, we could not figure where this distribution was available in R. It had to, since ks.test was returning a p-value. Hopefully correct! So I looked into the ks.test function, which happens not to be entirely programmed in C, and found the line

PVAL <- 1 - if (alternative == "two.sided") .Call(C_pKolmogorov2x, STATISTIC, n)

which means that the Kolmogorov distribution is coded as a C function C_pKolmogorov2x in R. However, I could not call the function myself.

> .Call(C_pKolmogorov2x,.3,4) Error: object 'C_pKolmogorov2x' not found

Hence, as I did not want to recode this distribution cdf, I posted the question on stackoverflow (long time no see!) and got a reply almost immediately as to use the package kolmim. Followed by the extra comment from the same person that calling the C code only required to add the path to its name, as in

> .Call(stats:::C_pKolmogorov2x,STAT=.3,n=4) [1] 0.2292
Filed under: Books, Kids, R, Statistics, University life Tagged: C code, importance sampling, Introducing Monte Carlo Methods with R, kolmim, Kolmogorov-Smirnov distance, R, stackoverflow, Université Paris Dauphine
Categories: Bayesian Bloggers

Cancun sunrise

Tue, 2014-07-15 12:40
Categories: Bayesian Bloggers

implementing reproducible research [short book review]

Mon, 2014-07-14 18:14

As promised, I got back to this book, Implementing reproducible research (after the pigeons had their say). I looked at it this morning while monitoring my students taking their last-chance R exam (definitely last chance as my undergraduate R course is not reconoduced next year). The book is in fact an edited collection of papers on tools, principles, and platforms around the theme of reproducible research. It obviously links with other themes like open access, open data, and open software. All positive directions that need more active support from the scientific community. In particular the solutions advocated through this volume are mostly Linux-based. Among the tools described in the first chapter, knitr appears as an alternative to sweave. I used the later a while ago and while I like its philosophy. it does not extend to situations where the R code within takes too long to run… (Or maybe I did not invest enough time to grasp the entire spectrum of sweave.) Note that, even though the book is part of the R Series of CRC Press, many chapters are unrelated to R. And even more [unrelated] to statistics.

This limitation is somewhat my difficulty with [adhering to] the global message proposed by the book. It is great to construct such tools that monitor and archive successive versions of code and research, as anyone can trace back the research steps conducting to the published result(s). Using some of the platforms covered by the book establishes for instance a superb documentation principle, going much further than just providing an “easy” verification tool against fraudulent experiments. The notion of a super-wiki where notes and preliminary versions and calculations (and dead ends and failures) would be preserved for open access is just as great. However this type of research processing and discipline takes time and space and human investment, i.e. resources that are sparse and costly. Complex studies may involve enormous amounts of data and, neglecting the notions of confidentiality and privacy, the cost of storing such amounts is significant. Similarly for experiments that require days and weeks of huge clusters. I thus wonder where those resources would be found (journals, universities, high tech companies, …?) for the principle to hold in full generality and how transient they could prove. One cannot expect the research time to garantee availability of those meta-documents for remote time horizons. Just as a biased illustration, checking the available Bayes’ notebooks meant going to a remote part of London at a specific time and with a preliminary appointment. Those notebooks are not available on line for free. But for how long?

“So far, Bob has been using Charlie’s old computer, using Ubuntu 10.04. The next day, he is excited to find the new computer Alice has ordered for him has arrived. He installs Ubuntu 12.04″ A. Davison et al.

Putting their principles into practice, the authors of Implementing reproducible research have made all chapters available for free on the Open Science Framework. I thus encourage anyone interesting in those principles (and who would not be?!) to peruse the chapters and see how they can benefit from and contribute to open and reproducible research.

Filed under: Books, Kids, pictures, R, Statistics, Travel, University life Tagged: Bayes' notebooks, book review, CHANCE, knitr, Linux, pigeon, R, R exam, reproducible research, sweave, Ubuntu 12.04, Université Paris Dauphine
Categories: Bayesian Bloggers

arrived in Cancún

Mon, 2014-07-14 09:30

After an uneventful trip from Paris, we landed to the heat and humidity just a day before our ABC course. Much too hot and too humid for my taste, so I am looking forward spending my days in the conference centre. Hopefully, it will get cool enough to go running in the early morning…

Most unfortunately, when trying to get a taste of the water last night, I almost immediately lost my prescription glasses to a big wave and am forced to move around the conference wearing sunglasses…. or looking lost and not recognizing anyone! What a bummer!

Filed under: Kids, pictures, Statistics, Travel, University life Tagged: Cancún, ISBA 2014, Mexico, sea, sunglasses
Categories: Bayesian Bloggers