Archive for UNSW

Approximate Bayesian analysis of (un)conditional copulas [webinar]

Posted in Books, pictures, Statistics, University life with tags , , , , , , , , , on September 17, 2020 by xi'an

The Algorithms & Computationally Intensive Inference seminar (access by request) will virtually resume this week in Warwick U on Friday, 18 Sept., at noon (UK time, ie +1GMT) with a talk by (my coauthor and former PhD student) Clara Grazian (now at UNSW), talking about approximate Bayes for copulas:

Many proposals are now available to model complex data, in particular thanks to the recent advances in computational methodologies and algorithms which allow to work with complicated likelihood function in a reasonable amount of time. However, it is, in general, difficult to analyse data characterized by complicated forms of dependence. Copula models have been introduced as probabilistic tools to describe a multivariate random vector via the marginal distributions and a copula function which captures the dependence structure among the vector components, thanks to the Sklar’s theorem, which states that any d-dimensional absolutely continuous density can be uniquely represented as the product of the marginal distributions and the copula function. Major areas of application include econometrics, hydrological engineering, biomedical science, signal processing and finance. Bayesian methods to analyse copula models tend to be computational intensive or to rely on the choice of a particular copula function, in particular because methods of model selection are not yet fully developed in this setting. We will present a general method to estimate some specific quantities of interest of a generic copula by adopting an approximate Bayesian approach based on an approximation of the likelihood function. Our approach is general, in the sense that it could be adapted both to parametric and nonparametric modelling of the marginal distributions and can be generalised in presence of covariates. It also allow to avoid the definition of the copula function. The class of algorithms proposed allows the researcher to model the joint distribution of a random vector in two separate steps: first the marginal distributions and, then, a copula function which captures the dependence structure among the vector components.


Scott Sisson’s ABC seminar in Paris [All about that Bayes]

Posted in pictures, Statistics, Travel, University life with tags , , , , , , , , , , , on January 20, 2020 by xi'an

On the “All about that Bayes” seminar tomorrow (Tuesday 21 at 3p.m., room 42, AgroParisTech, 16 rue Claude Bernard, Paris 5ième), Scott Sisson, School of Mathematics and Statistics at UNSW, and visiting Paris-Dauphine this month, will give a talk on

Approximate posteriors and data for Bayesian inference

For various reasons, including large datasets and complex models, approximate inference is becoming increasingly common. In this talk I will provide three vignettes of recent work. These cover a) approximate Bayesian computation for Gaussian process density estimation, b) likelihood-free Gibbs sampling, and c) MCMC for approximate (rounded) data.

common derivation for Metropolis–Hastings and other MCMC algorithms

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , , , , on July 25, 2016 by xi'an

Khoa Tran and Robert Kohn from UNSW just arXived a paper on a comprehensive derivation of a large range of MCMC algorithms, beyond Metropolis-Hastings. The idea is to decompose the MCMC move into

  1. a random completion of the current value θ into V;
  2. a deterministic move T from (θ,V) to (ξ,W), where only ξ matters.

If this sounds like a new version of Peter Green’s completion at the core of his 1995 RJMCMC algorithm, it is bedowntown Sydney from under Sydney Harbour bridge, July 15, 2012cause it is indeed essentially the same notion. The resort to this completion allows for a standard form of the Metropolis-Hastings algorithm, which leads to the correct stationary distribution if T is self-inverse. This representation covers Metropolis-Hastings algorithms, Gibbs sampling, Metropolis-within-Gibbs and auxiliary variables methods, slice sampling, recursive proposals, directional sampling, Langevin and Hamiltonian Monte Carlo, NUTS sampling, pseudo-marginal Metropolis-Hastings algorithms, and pseudo-marginal Hamiltonian  Monte Carlo, as discussed by the authors. Given this representation of the Markov chain through a random transform, I wonder if Peter Glynn’s trick mentioned in the previous post on retrospective Monte Carlo applies in this generic setting (as it could considerably improve convergence…)

ABC in Sydney, July 3-4, 2014!!!

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

Sydney Opera from Sydney Harbour Bridge, Sydney, July 14, 2012After ABC in Paris in 2009, ABC in London in 2011, and ABC in Roma last year, things are accelerating since there will be—as I just learned—  an ABC in Sydney next July (not June as I originally typed, thanks Robin!). The workshop on the current developments of ABC methodology thus leaves Europe to go down-under and to take advantage of the IMS Meeting in Sydney on July 7-10, 2014. Hopefully, “ABC in…” will continue its tour of European capitals in 2015! To keep up with an unbroken sequence of free workshops, Scott Sisson has managed to find support so that attendance is free of charge (free as in “no registration fee at all”!) but you do need to register as space is limited. While I would love to visit UNSW and Sydney once again and attend the workshop, I will not, getting ready for Cancún and our ABC short course there.

AMSI-SSAI Lectures #2-3

Posted in Books, pictures, Statistics, Travel, University life with tags , , , , , , , , , on July 18, 2012 by xi'an

The AMSI lecture at UNSW on ABC for model choice last Monday was very well-attended, with additional participants from other universities  like Newcastle connected through the grid, and Robert Kohn set a follow-up questions-and-answers session with local faculty and students. Interesting comments on pseudo-models and misspecified models… And new ideas for incoming Master projects. I am quite impressed by the School of Economics in the Australian School of Business and by the UNSW campus as a whole. (Maybe the more because a kind faculty who set me on my way there asked me from which part of England I was from!) This is the second econometrics department I visit this semester and I think we should beef up the interactions between stat and econ… (Maybe starting a Bayesian Econometric section at ISBA would help now that the Bayesian computation section has reached the critical level of support to be created!)

On Tuesday, I then took the train(s) to the University of Western Sydney, which allowed me to see much more of the greater Sydney than the (more privileged) area between downtown and Bondi! There were fewer peoples attending at UWS but video links with other campuses helped in reaching a critical level. Before returning to UNSW, I also managed to get a glimpse of one of Australia’s oldest buildings, a former girl orphanage built in 1813…

I must acknowledge some kind of “travel fatigue” syndrome at this stage of my trip, due both to a poor sleeping pattern and the constraints of staying a few days at a given place, with some difficulties to concentrate on deeper issues than planning the next move and not forgetting anything at the current one! I am thus looking forward the next fortnight in Monash University, Melbourne, where I am teaching a two-day course in addition to working with Gael Martin and giving a few seminars…