Archive for National University Singapore
36 hours in Singapore
Posted in Books, Statistics, Travel, University life with tags BayesComp 2025, chili crab, conference, foodies, hawker centres, IMS, Indonesia, Little India, Malaysia, MIchelin starred restaurant, nasi lemak, National University Singapore, Singapore, South Asian cuisine, tandoori cuisine, travel guide on May 22, 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.
O’Bayes 2025 [με αθηναϊκό άρωμα]
Posted in Statistics with tags Athens, Athens University of Economics and Business, BayesComp 2025, conference, foundations of objective Bayesian methodology, Greece, ISBA, ISBA conference, National Technical University of Athens, National University Singapore, O'Bayes, O'Bayes 2022, O'Bayes 2025, objective Bayes, Parthenon, Renzo Piano, scientific committee, Singapore, Stavros Niarchos Foundation, tutorial on November 6, 2024 by xi'an
The next O’Bayes conference will take place in Athens, 8-12 June 2025. Registration is open, as well as a call for posters. As in (all?) earlier episodes of these Objective Bayes methodology conferences, speakers and discussants are invited by the scientific committee. And the first afternoon consists of tutorials on different aspects of Objective Bayes methodology. The conference will take place in the Stavros Niarchos Foundation Cultural Centre. (Unfortunately, I will not be able to attend O’Bayes 2025, once again, due to my attending BayesComp 2025 the week after.)
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.)
