Archive for NUS
off to Singapore (BayesComp 2025)
Posted in pictures, Statistics, Travel, University life with tags AF256, BayesComp 2023, BayesComp 2025, Bayesian computational methods, conference, flight mode, Indian food, ISBA, Malay food, NUS, Singapore on June 16, 2025 by xi'an
registration open for BayesComp 2025
Posted in pictures, R, Statistics, Travel, University life with tags 1⁰ North, BayesComp 2025, Bayesian computational methods, ISBA, National University Singapore, NUS, poster session, registration, satellite workshop, Singapore, South East Asia on January 29, 2025 by xi'an
The registration for the incoming, exciting, Bayes Comp 2025 conference (and its satellites) is now open, including information regarding accommodations for the conference. Early bird rates run till 15 March. Furthermore, the call for contributed talks is open till 17 February.
BayesComp²⁵ in Singapore
Posted in pictures, Statistics, Travel with tags Arctic, BayesComp 2025, Bayesian computing, conference, equator, ISBA, Levi, National University Singapore, NUS, Singapore on August 28, 2023 by xi'an
variational approximation to empirical likelihood ABC
Posted in Statistics with tags ABC, Approximate Bayesian computation, data generating process, differential entropy, empirical likelihood, g-and-k distributions, information projection, k-nearest neighbours, Kozachenko-Leonenko estimator, marginal likelihood identity, National Academy of Science, National University Singapore, NUS, PNAS, SAME algorithm, Singapore, Statisfaction, variational approximations, winference on October 1, 2021 by xi'an
Sanjay Chaudhuri and his colleagues from Singapore arXived last year a paper on a novel version of empirical likelihood ABC that I hadn’t yet found time to read. This proposal connects with our own, published with Kerrie Mengersen and Pierre Pudlo in 2013 in PNAS. It is presented as an attempt at approximating the posterior distribution based on a vector of (summary) statistics, the variational approximation (or information projection) appearing in the construction of the sampling distribution of the observed summary. (Along with a weird eyed-g symbol! I checked inside the original LaTeX file and it happens to be a mathbbmtt g, that is, the typewriter version of a blackboard computer modern g…) Which writes as an entropic correction of the true posterior distribution (in Theorem 1).
“First, the true log-joint density of the observed summary, the summaries of the i.i.d. replicates and the parameter have to be estimated. Second, we need to estimate the expectation of the above log-joint density with respect to the distribution of the data generating process. Finally, the differential entropy of the data generating density needs to be estimated from the m replicates…”
The density of the observed summary is estimated by empirical likelihood, but I do not understand the reasoning behind the moment condition used in this empirical likelihood. Indeed the moment made of the difference between the observed summaries and the observed ones is zero iff the true value of the parameter is used in the simulation. I also fail to understand the connection with our SAME procedure (Doucet, Godsill & X, 2002), in that the empirical likelihood is based on a sample made of pairs (observed,generated) where the observed part is repeated m times, indeed, but not with the intent of approximating a marginal likelihood estimator… The notion of using the actual data instead of the true expectation (i.e. as a unbiased estimator) at the true parameter value is appealing as it avoids specifying the exact (or analytical) value of this expectation (as in our approach), but I am missing the justification for the extension to any parameter value. Unless one uses an ancillary statistic, which does not sound pertinent… The differential entropy is estimated by a Kozachenko-Leonenko estimator implying k-nearest neighbours.
“The proposed empirical likelihood estimates weights by matching the moments of g(X¹), …, g(X⁹) with that of
g(X⁰), without requiring a direct relationship with the parameter. (…) the constraints used in the construction of the empirical likelihood are based on the identity in (7), which can only be satisfied when θ = θ⁰. “
Although I am feeling like missing one argument, the later part of the paper seems to comfort my impression, as quoted above. Meaning that the approximation will fare well only in the vicinity of the true parameter. Which makes it untrustworthy for model choice purposes, I believe. (The paper uses the g-and-k benchmark without exploiting Pierre Jacob’s package that allows for exact MCMC implementation.)
Nature tidbits
Posted in Books, Statistics, University life with tags @ScientistTrump, abortion, AI, Argentina, Donald Trump, EPA, legalisation of abortion, machine learning, NASA, NUS, retirement, survey sampling on September 18, 2018 by xi'an
network, to Louis Chen from NUS being mentioned in a career article about the importance of planning well in advance one’s retirement to preserve academia links and manage a new position or even career. Which is what happened to Louis as he stayed head of NUS after the mandatory retirement age and is now emeritus and still engaged into research. (The article made me wonder however how the cases therein had be selected.) It is actually most revealing to see how different countries approach the question of retirements of academics: in France, for instance, one is essentially forced to retire and, while there exist emeritus positions, it is extremely difficult to find funding.
“Louis Chen was technically meant to retire in 2005. The mathematician at the National University of Singapore was turning 65, the university’s official retirement age. But he was only five years into his tenure as director of the university’s new Institute for Mathematical Sciences, and the university wanted him to stay on. So he remained for seven more years, stepping down in 2012. Over the next 18 months, he travelled and had knee surgery, before returning in summer 2014 to teach graduate courses for a year.”
And [yet] another piece on the biases of AIs. Reproducing earlier papers discussed here, with one obvious reason being that the learning corpus is not representative of the whole population, maybe survey sampling should become compulsory in machine learning training degrees. And yet another piece on why protectionism is (also) bad for the environment.
