brutally sent to Coventry

statistical inference and uncertainty quantification for complex process-based models using multiple data sets [postdoc in Warwick]

Applications are invited for a postdoctoral research fellow position to work on the project “Statistical inference and uncertainty quantification for complex process-based models using multiple data sets”.

The position is part of a project to develop, implement and apply methods for parameter inference of models of environmental processes: particularly approaches based on approximate Bayesian computation (ABC) and particle MCMC.

The project is funded through the UKRI Strategic Priorities Fund programme on “Landscape Decisions: Towards a new framework for using land assets”. This programme will address the challenge of delivering better, evidence-based decisions within UK landscapes through research collaboration with policy, business and land management partners to deliver an interdisciplinary decision-making framework to inform how land is used. The post holder will become part of the world-renowned Department of Statistics at the University of Warwick.

Informal enquires can be addressed to Dr Richard Everitt, with closing date 8 March 2020.

O’Bayes 19/4

Last talks of the conference! With Rui Paulo (along with Gonzalo Garcia-Donato) considering the special case of factors when doing variable selection. Which is an interesting question that I had never considered, as at best I would remove all leves or keeping them all. Except that there may be misspecification in the factors as for instance when several levels have the same impact.With Michael Evans discussing a paper that he wrote for the conference! Following his own approach to statistical evidence. And including his reluctance to cover infinity (calling on Gauß for backup!) or continuity, and his call to falsify a Bayesian model by checking it can be contradicted by the data. His assumption that checking for prior is separable from checking for [sampling] model is debatable. (With another mention made of the Savage-Dickey ratio.)

And with Dimitris Fouskakis giving a wide ranging assessment [which Mark Steel (Warwick) called a PEP talk!] of power-expected-posterior priors, used with reference (and usually improper) priors. Which in retrospect would have suited better the beginning of the conference as it provided a background to several of the talks. Raising a question (from my perspective) on using the maximum likelihood estimator as a pseudo-sufficient statistic when this MLE is computed for the base (simplest) model. Maybe an ABC induced bias in this question as it would not work for ABC model choice.

Overall, I think the scientific outcomes of the conference were quite positive: a wide range of topics and perspectives, a reasonable and diverse attendance, especially when considering the heavy load of related conferences in the surrounding weeks (the “June fatigue”!), animated poster sessions. I am obviously not the one to assess the organisation of the conference! Things I forgot to do in this regard: organise transportation from Oxford to Warwick University, provide an attached room for in-pair research, insist on sustainability despite the imposed catering solution, facilitate sharing joint transportation to and from the Warwick campus, mention that tap water was potable, and… wear long pants when running in nettles.

O’Bayes 19/3

Nancy Reid gave the first talk of the [Canada] day, in an impressive comparison of all approaches in statistics that involve a distribution of sorts on the parameter, connected with the presentation she gave at BFF4 in Harvard two years ago, including safe Bayes options this time. This was related to several (most?) of the talks at the conference, given the level of worry (!) about the choice of a prior distribution. But the main assessment of the methods still seemed to be centred on a frequentist notion of calibration, meaning that epistemic interpretations of probabilities and hence most of Bayesian answers were disqualified from the start.

In connection with Nancy’s focus, Peter Hoff’s talk also concentrated on frequency valid confidence intervals in (linear) hierarchical models. Using prior information or structure to build better and shrinkage-like confidence intervals at a given confidence level. But not in the decision-theoretic way adopted by George Casella, Bill Strawderman and others in the 1980’s. And also making me wonder at the relevance of contemplating a fixed coverage as a natural goal. Above, a side result shown by Peter that I did not know and which may prove useful for Monte Carlo simulation.

Jaeyong Lee worked on a complex model for banded matrices that starts with a regular Wishart prior on the unrestricted space of matrices, computes the posterior and then projects this distribution onto the constrained subspace. (There is a rather consequent literature on this subject, including works by David Dunson in the past decade of which I was unaware.) This is a smart demarginalisation idea but I wonder a wee bit at the notion as the constrained space has measure zero for the larger model. This could explain for the resulting posterior not being a true posterior for the constrained model in the sense that there is no prior over the constrained space that could return such a posterior. Another form of marginalisation paradox. The crux of the paper is however about constructing a functional form of minimaxity. In his discussion of the paper, Guido Consonni provided a representation of the post-processed posterior (P³) that involves the Dickey-Savage ratio, sort of, making me more convinced of the connection.

As a lighter aside, one item of local information I should definitely have broadcasted more loudly and long enough in advance to the conference participants is that the University of Warwick is not located in ye olde town of Warwick, where there is no university, but on the outskirts of the city of Coventry, but not to be confused with the University of Coventry. Located in Coventry.

 

abseiling down Coventry cathedral