Archive for philogenic trees

Mark [a]B[c], plus cats

Posted in Books, Kids, pictures, Statistics, University life with tags , , , , , , , , , , , , , , , , , , , on June 22, 2024 by xi'an

30 May was a day of first and last times, if not in capital ways (for me), on the One World ABC webinar. This was the first time we had a talk by Mark Beaumont and also the first time I had team experience of facing a smoking participant, while this was the last time of our monthly webinar for the (Northern) academic year.

The talk was about model misspecification in population genomic, from an ABC perspective with the motivation of common noticeable difference between the distributions of d(s,s⁰) and d(s,s’), distances between the prior predictively simulated summary statistics and the observed ones vs posterior generated ones, which should indicates misspecification, esp with complicated models. Mark and his coauthors then supported a gradual elimination of summary statistics to diminish the discrepancy, hence voluntarily impoverishing the model. While blaming the statistics sounded a bit like shooting the messenger, the resolution is of obvious interest if backing from modelling the misspecification itself.

Some of the presented work was conducted in Ward et al (2022, NeurIPS) with a reference to the outlying Ratmann et al (2009) we later discussed, for including tolerance as an extra parameter ε, thereby reconsidering Wikinson’s exact ABC for noisy observations y by the medium of a normalising flow on the marginal distribution of the denoised x (learned from the prior predictive)

More precisely, the idea is to drop summary statistics by checking whether or not the observed S⁰ belongs to HPD region, removing one component of S at a time, using e.g. a k-NN estimate for the summary density (hence depending on parameterisation of said statistics for the distance). Hopefully, the process stops before loosing identifiability by using too few statistics. I also wondered at multiple uses of the data in this sequential procedure but Mark argued for adopting a meta- or pragma- or Gelmanian- Bayesian perspective in the end!

Another perk was the appearance of (and illustration with) the Scottish Wildcat, mentioned in The Guardian a few months ago and discussed in the ‘Og, with further papers exploring more aspects of this hybridization, like a posterior applied to a much more complex phylogenic tree reconstruction for cats of different creeds and many related parameters.

potato tomato [w/o ABC]

Posted in Books, pictures, Statistics with tags , , , , , , on July 8, 2022 by xi'an

“The default parameter of KaKs_Calculator was set to estimate the Ka/Ks values, which means that the Ka/Ks value was the average of the output from 15 available algorithms comprising 7 original approximate methods and one maximum likelihood method.”

In their analysis of the philogenic evolution of the potato species, the Nature authors resort to a multiple analysis (à la EJ!) in the above sense, by averaging several results. I remain puzzled by the approach that treats all methods on an equal basis, without trying to ascertain precision and bias by X validation or other tools. (Approximate Bayesian Computing was not used as one of the methods.)

JSM 2015 [day #4]

Posted in pictures, Running, Statistics, Travel, University life with tags , , , , , , , , , , , , , , , , , , on August 13, 2015 by xi'an

My first session today was Markov Chain Monte Carlo for Contemporary Statistical Applications with a heap of interesting directions in MCMC research! Now, without any possible bias (!), I would definitely nominate Murray Pollock (incidentally from Warwick) as the winner for best slides, funniest presentation, and most enjoyable accent! More seriously, the scalable Langevin algorithm he developed with Paul Fearnhead, Adam Johansen, and Gareth Roberts, is quite impressive in avoiding computing costly likelihoods. With of course caveats on which targets it applies to. Murali Haran showed a new proposal to handle high dimension random effect models by a projection trick that reduces the dimension. Natesh Pillai introduced us (or at least me!) to a spectral clustering that allowed for an automated partition of the target space, itself the starting point to his parallel MCMC algorithm. Quite exciting, even though I do not perceive partitions as an ideal solution to this problem. The final talk in the session was Galin Jones’ presentation of consistency results and conditions for multivariate quantities which is a surprisingly unexplored domain. MCMC is still alive and running!

The second MCMC session of the morning, Monte Carlo Methods Facing New Challenges in Statistics and Science, was equally diverse, with Lynn Kuo’s talk on the HAWK approach, where we discovered that harmonic mean estimators are still in use, e.g., in MrBayes software employed in phylogenetic inference. The proposal to replace this awful estimator that should never be seen again (!) was rather closely related to an earlier solution of us for marginal likelihood approximation, based there on a partition of the whole space rather than an HPD region in our case… Then, Michael Betancourt brilliantly acted as a proxy for Andrew to present the STAN language, with a flashy trailer he most recently designed. Featuring Andrew as the sole actor. And with great arguments for using it, including the potential to run expectation propagation (as a way of life). In fine, Faming Liang proposed a bootstrap subsampling version of the Metropolis-Hastings algorithm, where the likelihood acknowledging the resulting bias in the limiting distribution.

My first afternoon session was another entry on Statistical Phylogenetics, somewhat continued from yesterday’s session. Making me realised I had not seen a single talk on ABC for the entire meeting! The issues discussed in the session were linked with aligning sequences and comparing  many trees. Again in settings where likelihoods can be computed more or less explicitly. Without any expertise in the matter, I wondered at a construction that would turn all trees, like  into realizations of a continuous model. For instance by growing one branch at a time while removing the MRCA root… And maybe using a particle like method to grow trees. As an aside, Vladimir Minin told me yesterday night about genetic mutations that could switch on and off phenotypes repeatedly across generations… For instance  the ability to glow in the dark for species of deep sea fish.

When stating that I did not see a single talk about ABC, I omitted Steve Fienberg’s Fisher Lecture R.A. Fisher and the Statistical ABCs, keeping the morceau de choix for the end! Even though of course Steve did not mention the algorithm! A was for asymptotics, or ancilarity, B for Bayesian (or biducial??), C for causation (or cuffiency???)… Among other germs, I appreciated that Steve mentioned my great-grand father Darmois in connection with exponential families! And the connection with Jon Wellner’s LeCam Lecture from a few days ago. And reminding us that Savage was a Fisher lecturer himself. And that Fisher introduced fiducial distributions quite early. And for defending the Bayesian perspective. Steve also set some challenges like asymptotics for networks, Bayesian model assessment (I liked the notion of stepping out of the model), and randomization when experimenting with networks. And for big data issues. And for personalized medicine, building on his cancer treatment. No trace of the ABC algorithm, obviously, but a wonderful Fisher’s lecture, also most obviously!! Bravo, Steve, keep thriving!!!

Ebola virus [and Mr. Bayes]

Posted in Statistics, Travel, University life with tags , , , , , , , on August 12, 2014 by xi'an

Just like after the Malaysian Airlines flight 370 disappearance, the current Ebola virus outbreak makes me feel we are sorely missing an emergency statistical force to react on urgent issues… It would indeed be quite valuable to have a team of statisticians at the ready to quantify risks and posterior probabilities and avoid media approximations. The situations calling for this reactive force abound. A few days ago I was reading about the unknown number of missing pro-West activists in Eastern Ukraine. Maybe statistical societies could join forces to set such an emergency team?! Whose goals are somewhat different from the great Statistics without Borders…

As a side remark, the above philogeny is taken from Dudas and Rambaut’s recent paper in PLOS reassessing the family tree of the current Ebola virus(es) acting in Guinea. The tree is found using MrBayes, which delivers a posterior probability of 1 to this filiation! And concluding “that the rooting of this clade using the very divergent other ebolavirus species is very problematic.”

Advances in scalable Bayesian computation [day #4]

Posted in Books, Mountains, pictures, R, Statistics, University life with tags , , , , , , , , , , , , , , , , , on March 7, 2014 by xi'an

polyptych painting within the TransCanada Pipeline Pavilion, Banff Centre, Banff, March 21, 2012Final day of our workshop Advances in Scalable Bayesian Computation already, since tomorrow morning is an open research time ½ day! Another “perfect day in paradise”, with the Banff Centre campus covered by a fine snow blanket, still falling…, and making work in an office of BIRS a dream-like moment.

Still looking for a daily theme, parallelisation could be the right candidate, even though other talks this week went into parallelisation issues, incl. Steve’s talk yesterday. Indeed, Anthony Lee gave a talk this morning on interactive sequential Monte Carlo, where he motivated the setting by a formal parallel structure. Then, Darren Wilkinson surveyed the parallelisation issues in Monte Carlo, MCMC, SMC and ABC settings, before arguing in favour of a functional language called Scala. (Neat entries to those topics can be found on Darren’s blog.) And in the afternoon session, Sylvia Frühwirth-Schnatter exposed her approach to the (embarrassingly) parallel problem, in the spirit of Steve’s , David Dunson’s and Scott’s (a paper posted on the day I arrived in Chamonix and hence I missed!). There was plenty to learn from that talk (do not miss the Yin-Yang moment at 25 mn!), but it also helped me to break a difficulty I had with the consensus Bayes representation for two weeks (more on that later!). And, even though Marc Suchard mostly talked about flu and trees in a very pleasant and broad talk, he also had a slide on parallelisation to fit the theme! Although unrelated with parallelism,  Nicolas Chopin’s talk was on sequential quasi-Monte Carlo algorithms: while I had heard previous versions of this talk in Chamonix and BigMC, I found it full of exciting stuff. And it clearly got the room truly puzzled by this possibility, in a positive way! Similarly, Alex Lenkoski spoke about extreme rain events in Norway with no trace of parallelism, but the general idea behind the examples was to question the notion of the calibrated Bayesian (with possible connections with the cut models).

This has been a wonderful week and I am sure the participants got as much as I did from the talks and the informal exchanges. Thanks to BIRS for the sponsorship and the superb organisation of the week (and to the Banff Centre for providing such a paradisical environment). I feel very privileged to have benefited from this support, even though I deadly hope to be back in Banff within a few years.