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The LiU Seminar Series in Statistics and Mathematical Statistics

Tuesday, February 7, 4.15 pm, 2023. Seminar in Mathematical Statistics.

Simulation of random fields on Riemannian manifolds
Annika Lang
Department of Mathematical Science, Chalmers University of Technology
Abstract:Random fields are important building blocks in spatial models disturbed by randomness such as solutions to stochastic partial differential equations. The fast simulation of random fields is therefore crucial for efficient algorithms in uncertainty quantification. In this talk I present numerical methods for Gaussian random fields on Riemannian manifolds and discuss their convergence. Simulations illustrate the theoretical findings.
This talk is based on joint work with Erik Jansson, Mihály Kovács, and Mike Pereira.
Location: Online via Zoom. Please email Krzysztof Bartoszek for invitation to Zoom meeting.

Tuesday, February 21, 3.15 pm, 2023. Seminar in Statistics.

Bayesian Borrowing Between Sub-Populations … and other topics
Carl-Fredrik Burman
, AstraZeneca, Göteborg
Abstract: In this seminar, we will briefly discuss a few topics from pharmaceutical statistics that may be of interest to a wider statistics / data science audience.
We will also dive a bit deeper into a problem of Bayesian borrowing of information. With the emergence of personalised medicine and targeted drugs, it is increasingly common that a drug is expected to work better in one disease subpopulation, B, than in its complement, C. If the placebo-adjusted effect in B is positive, this may spill over to a smaller but positive effect in C. Many trials are not powered to demonstrate a clear effect in C in its own right. Still, it is of great importance to determine whether the drug should receive marketing authorisation, reimbursement and wide prescriptions in C. As it makes sense to borrow information between B and C, we will explore whether a pre-specified prior for the ratio of the effects in C and B may aid the analysis and facilitate decision making. Following traditional regulatory practise, we strive to minimise assumptions. While we need an informative prior to connect the effects in C and B, we use a non-informative prior for overall efficacy.
This work triggers discussions about both Bayesian methods and information borrowing in general. Our setting constitutes one of the simplest possible situations for Bayesian borrowing. The issues that we experience in this setting, such as the discrepancy between Bayesian and frequentist approaches, the impossibility of finding a truly non-informative prior, and the type 1 error inflation, will often be issues in other more complicated situations as well.
Location: Alan Turing.

Tuesday, March 21, 3.15 pm, 2023. Seminar in Statistics.

Scaling limit of Markov chain/process Monte Carlo methods
Kengo Kamatani
, The Institute of Statistical Mathematics
Abstract: The scaling limit analysis of Markov Chain Monte Carlo methods has been a topic of intensive study in recent decades. The analysis entails determining the rate at which the Markov Chain converges to its limiting process, typically a Langevin diffusion process, and provides useful guidelines for parameter tuning. Since the seminal work of Roberts et al. in 1997, numerous researchers have generalized the original assumptions and expanded the results to more sophisticated methods. Recently, there has been growing interest in piecewise deterministic Markov processes for Monte Carlo integration methods, particularly the Bouncy Particle Sampler and the Zig-Zag Sampler. This talk will focus on determining the scaling limits for both algorithms and provide a criterion for tuning the Bouncy Particle Sampler. This is joint work with J. Bierkens (TU Delft) and G. O. Roberts (Warwick).
Location: KEY1 (Key Building).

Tuesday, April 25, 3.15 pm, 2023. Seminar in Statistics.

Rebecka Jörnsten
, Department of Mathematical Sciences, University of Gothenburg
Location: Alan Turing.

Tuesday, May 2, 3.15 pm, 2023. Seminar in Statistics.

Patrik Rydén
, Department of Mathematics and Mathematical Statistics, Umeå University
Location: Alan Turing.

Tuesday, May 16, 3.15 pm, 2023. Seminar in Statistics.

Justyna Signerska-Rynkowska
, Dioscuri Centre in Topological Data Analysis, Institute of Mathematics of the Polish Academy of Sciences
Location: Alan Turing.


Past Seminars

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Spring 2012 | Fall 2011

Page responsible: Krzysztof Bartoszek
Last updated: 2023-03-09