Archive for University of Oxford

Metropolis-Hastings via classification

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , , on February 23, 2021 by xi'an

Veronicka Rockova (from Chicago Booth) gave a talk on this theme at the Oxford Stats seminar this afternoon. Starting with a survey of ABC, synthetic likelihoods, and pseudo-marginals, to motivate her approach via GANs, learning an approximation of the likelihood from the GAN discriminator. Her explanation for the GAN type estimate was crystal clear and made me wonder at the connection with Geyer’s 1994 logistic estimator of the likelihood (a form of discriminator with a fixed generator). She also expressed the ABC approximation hence created as the actual posterior times an exponential tilt. Which she proved is of order 1/n. And that a random variant of the algorithm (where the shift is averaged) is unbiased. Most interestingly requiring no calibration and no tolerance. Except indirectly when building the discriminator. And no summary statistic. Noteworthy tension between correct shape and correct location.

a journal of the plague year [almost gone]

Posted in Books, Kids, Mountains, pictures, Travel with tags , , , , , , , , , , , , , , , , , , , , , , , , , , , , on January 23, 2021 by xi'an

Read The stars are legion, by Kameron Hurley, which I brought back from Gainesville last year. Although I cannot remember why I bought the book, it must have been a “recommendation” on Amazon… The story is part unusual, part classical, with a constant switch between the two major characters [viewpoint].  And between different times. The style is complex, maybe too complex, as the universe is slowly revealing itself, through the perception biases of the characters. Including (spoiler!) one with multiple memory erasures and two attempts at recycling. Stars are actually (spoiler!) space-ships with some possibly organic elements that are decomposing (and showing the steel skeletons), with also apparently organic smaller vessels to travel between ships or fight between clans. Some of the ship inhabitants are mutants, possibly for being unprotected from space or ship radiations (although the control and propulsion of these ships is never mentioned), possibly because they are perceived as such by different groups in the ships, à la Huxley’s Brave New World? And there seem to be only females on-board, with all of them getting (mysteriously) pregnant at one time or another, rarely giving birth to children (associated with driving the ships? creating new ships?) but rather to other organic entities, apparently contributing to keeping the ship alive. All this is quite creative, with a powerful theme of power versus motherhood, but the story-telling is just too messy for me to have enjoyed it. The more because the type of subterranean universe where characters wander from one level to the next and discover supremely different ecosystems at each level never appealed to me. Since I read Verne’s Voyage au Centre de la Terre. (And I suddenly remembered dropping out of an earlier Hurley’s book.)

Cooked (the last remaining) pumpkin risotto with (legal) Lapsang tea, which worked out rather nicely, albeit loosing most of the Lapsang flavour. Had a week of (pleasant) cookie flavour home fragrance while my wife was preparing cookies for the entire family. Cooked a brunch with my son on the last Sunday of 2020, once again with Lapsang as drink. And had a Michelin take-away with my mom in Caen, since all restaurants remain closed till an unknown date. Which proved a great choice as it was surprisingly good, once out of the (potato starch) package.

Watched Season 2 of the BBC His Dark Materials series. Still impressed by the high level of the show (and enjoying it even more as I had forgotten basically everything about The Subtle Knife!) Except for the dark matter physicist turning to I Ching to understand her empirical experiment… But it remains a great series (esp. when mostly avoiding bears.) Also rewatched a Harry Potter film with my daughter, The Order of the Phoenix, which I found rather poor on the whole, despite a few great scenes (like the Wesley twins’ departure) and the fabulous rendering of the petty bureaucratic evil of Mrs. Umbridge throughout the film. And a part of The Half Blood Prince. Which sounded much better by comparison.

“It slowly dawned on me that it’s possible for the wise men who run your life for you to see disaster coming and not have a plan for dealing with it”

Read another K.J. Parker’s book, “How to rule an empire and get away with it“, sequel to “Sixteen ways &tc.” Light (mind-candy) but enjoyable bedside reading. Somewhat of a classical trick where a double becomes the real thing, if not in a Kagemusha tragic style.

simulating the pandemic

Posted in Books, Statistics with tags , , , , , , , , , , , on November 28, 2020 by xi'an

Nature of 13 November has a general public article on simulating the COVID pandemic as benefiting from the experience gained by climate-modelling methodology.

“…researchers didn’t appreciate how sensitive CovidSim was to small changes in its inputs, their results overestimated the extent to which a lockdown was likely to reduce deaths…”

The argument is essentially Bayesian, namely rather than using a best guess of the parameters of the model, esp. given the state of the available data (and the worse for March). When I read

“…epidemiologists should stress-test their simulations by running ‘ensemble’ models, in which thousands of versions of the model are run with a range of assumptions and inputs, to provide a spread of scenarios with different probabilities…”

it sounds completely Bayesian. Even though there is no discussion of the prior modelling or of the degree of wrongness of the epidemic model itself. The researchers at UCL who conducted the multiple simulations and the assessment of sensitivity to the 940 various parameters found that 19 of them had a strong impact, mostly

“…the length of the latent period during which an infected person has no symptoms and can’t pass the virus on; the effectiveness of social distancing; and how long after getting infected a person goes into isolation…”

but this outcome is predictable (and interesting). Mentions of Bayesian methods appear at the end of the paper:

“…the uncertainty in CovidSim inputs [uses] Bayesian statistical tools — already common in some epidemiological models of illnesses such as the livestock disease foot-and-mouth.”

and

“Bayesian tools are an improvement, says Tim Palmer, a climate physicist at the University of Oxford, who pioneered the use of ensemble modelling in weather forecasting.”

along with ensemble modelling, which sounds a synonym for Bayesian model averaging… (The April issue on the topic had also Bayesian aspects that were explicitely mentionned.)

MCqMC 2020 live and free and online

Posted in pictures, R, Statistics, Travel, University life with tags , , , , , , , , , , , , , on July 27, 2020 by xi'an

The MCqMC 20202 conference that was supposed to take place in Oxford next 9-14 August has been turned into an on-line free conference since travelling remains a challenge for most of us. Tutorials and plenaries will be live with questions  on Zoom, with live-streaming and recorded copies on YouTube. They will probably be during 14:00-17:00 UK time (GMT+1),  15:00-18:00 CET (GMT+2), and 9:00-12:00 ET. (Which will prove a wee bit of a challenge for West Coast and most of Asia and Australasia researchers, which is why our One World IMS-Bernoulli conference we asked plenary speakers to duplicate their talks.) All other talks will be pre-recorded by contributors and uploaded to a website, with an online Q&A discussion section for each. As a reminder here are the tutorials and plenaries:

Invited plenary speakers:

Aguêmon Yves Atchadé (Boston University)
Jing Dong (Columbia University)
Pierre L’Écuyer (Université de Montréal)
Mark Jerrum (Queen Mary University London)
Peter Kritzer (RICAM Linz)
Thomas Muller (NVIDIA)
David Pfau (Google DeepMind)
Claudia Schillings (University of Mannheim)
Mario Ullrich (JKU Linz)

Tutorials:

Fred Hickernell (IIT) — Software for Quasi-Monte Carlo Methods
Aretha Teckentrup (Edinburgh) — Markov chain Monte Carlo methods

non-reversible jump MCMC

Posted in Books, pictures, Statistics with tags , , , , , , , on June 29, 2020 by xi'an

Philippe Gagnon and et Arnaud Doucet have recently arXived a paper on a non-reversible version of reversible jump MCMC, the methodology introduced by Peter Green in 1995 to tackle Bayesian model choice/comparison/exploration. Whom Philippe presented at BayesComp20.

“The objective of this paper is to propose sampling schemes which do not suffer from such a diffusive behaviour by exploiting the lifting idea (…)”

The idea is related to lifting, creating non-reversible behaviour by adding a direction index (a spin) to the exploration of the models, assumed to be totally ordered, as with nested models (mixtures, changepoints, &tc.).  As with earlier versions of lifting, the chain proceeds along one (spin) direction until the proposal is rejected in which case the spin spins. The acceptance probability in the event of a change of model (upwards or downwards) is essentially the same as the reversible one (meaning it includes the dreaded Jacobian!). The original difficulty with reversible jump remains active with non-reversible jump in that the move from one model to the next must produce plausible values. The paper recalls two methods proposed by Christophe Andrieu and his co-authors. One consists in buffering a tempering sequence, but this proves costly.  Pursuing the interesting underlying theme that both reversible and non-reversible versions are noisy approximations of the marginal ratio, the other one consists in marginalising out the parameter to approximate the marginal probability of moving between nearby models. Combined with multiple choice to preserve stationarity and select more likely moves at the same time. Still requiring a multiplication of the number of simulations but parallelisable. The paper contains an exact comparison result that non-reversible jump leads to a smaller asymptotic variance than reversible jump, but it is unclear to me whether or not this accounts for the extra computing time resulting from the multiple paths in the proposed algorithms. (Even though the numerical illustration shows an improvement brought by the non-reversible side for the same computational budget.)

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