Mostly Monte Carlo Se[a]minar

A brand new monthly series of Parisian seminars on the theory and practice of Monte Carlo in statistics and data science, in conjunction with our ERC OCEAN project. To kick start the series the organisers, Joshua Bon and Andrea Bertazzi, first postdocs in the project, will present some of their work on Friday 13 October, 4PM – 6PM, Room 7, PariSanté Campus 2 Rue d’Oradour-sur-Glane, Paris 15. The following seminars are planned on Friday 17 November and Friday 15 December.

4pm/16h CEST: Piecewise deterministic sampling with splitting schemes

Andrea Bertazzi, CMAP – École Polytechnique

Piecewise deterministic Markov processes (PDMPs) received substantial interest in recent years as an alternative to classical Markov chain Monte Carlo algorithms. While theoretical properties of PDMPs have been studied extensively, their practical implementation remains limited to specific applications in which bounds on the gradient of the negative log-target can be derived. In order to address this problem, we propose to approximate PDMPs using splitting schemes, that means simulating the deterministic dynamics and the random jumps in two different stages. We show that symmetric splittings of PDMPs are of second order. Then we focus on the Zig-Zag sampler (ZZS) and show how to remove the bias of the splitting scheme with a skew reversible Metropolis filter. Finally, we illustrate with numerical simulations the advantages of our proposed scheme over competitors.

5pm/17h CEST: Bayesian score calibration for approximate models

Joshua Bon, Ceremade – Université Paris Dauphine-PSL

Scientists continue to develop increasingly complex mechanistic models to reflect their knowledge more realistically. Statistical inference using these models can be challenging since the corresponding likelihood function is often intractable and model simulation may be computationally burdensome. Fortunately, in many of these situations, it is possible to adopt a surrogate model or approximate likelihood function. It may be convenient to base Bayesian inference directly on the surrogate, but this can result in bias and poor uncertainty quantification. In this paper we propose a new method for adjusting approximate posterior samples to reduce bias and produce more accurate uncertainty quantification. We do this by optimizing a transform of the approximate posterior that maximizes a scoring rule. Our approach requires only a (fixed) small number of complex model simulations and is numerically stable. We demonstrate good performance of the new method on several examples of increasing complexity.

a first 5k [since last one]

cost(s) of living

Yesterday, Andrew posted an announcement for a postdoc position in Paris, at the national medical research institute (INSERM) on Bayesian approaches to high throughput genetic analyses using nonlinear mixed effect models and the comments went ballistic about the low salary attached to this postdoctoral position, namely 2600€ – 3000€. As I have already commented on the rather stale clichés on French academics, let me briefly reflect on the limitations of comparing 3000€ a month in Paris with say $5000 a month in New York City. (Which seems to be at the high end of US postdoc salaries.) First, the posted salaries are “gross” but the French one already excludes the 25% taxes paid by the employer. I do not know if this is the case in the US. Second, comparing absolute values makes little sense imho. Even if the purchasing power parity is about one between France and the US, I think the long term cost of living [as opposed to visiting for a week] is lower here than there. If only because the amount is similar to, if higher than, the starting academic salaries and around the median salary. Interestingly, the same appears to be true for the US, if less favourably for the postdocs there.

difficult times for postdocs

In the plane to Warwick on Monday, I was reading my latest issue of Nature and found an interesting editorial on the financial plight of many graduates and post-docs in both the US and the UK (and certainly elsewhere). Who, despite having a fellowship, cannot make ends meet. This is particularly true in expensive cities like London, Oxford or even Paris, where rents force those new researchers to face long commuting hours. The editorial suggests taking extra-jobs to make up for financial difficulties, but this does not sound to me like a particularly pertinent recommendation if it means taking time off one’s research, at the period in a researcher’s career where one’s energy should be mostly directed at the production of papers towards securing a (more) permanent job. Even teaching can prove too time consuming for finishing PhD students. An adequation between the needs of those young researchers and the institutional support they receive would sound like a natural requirement, while graduates looking for fellowship should truly assess the adequation in detail before accepting an offer.Which of course is not always easy. In countries where post-doctoral contracts are not negotiable and are set at a national level (like, e.g., France), checking with earlier fellows is a must. (As it happens or happened, I was quite lucky to spend my post-doctoral years in cheap places with decent support from the local universities, but this is not relevant in today’s environment!)

postdoctoral positions in Paris

There is a call for postdoctoral positions supported by the Paris Mathematical Sciences Foundation. The deadline is December 13 and the on-line application is available. If you are interested in working with me on Bayesian statistics  (model choice, time series model) or computational methods (SMC, MCMC, ABC, &c.) thru this call, please contact me at bayesianstatistics [à] gmail.com. There are 20 positions, so the position is not guaranteed but Robin Ryder applied two years ago and did get a one year  funding from the foundation.