Seminar series in Probability and Statistics
We are happy to announce that our seminar series is back to its physical, on-campus format.
The Seminar Series in Probability and Statistics is a lecture series of invited speakers in the fields of probability, statistics, and related fields, organized every second week. The talks are in seminar format, aimed at a broad audience ranging from graduate students of probability and statistics to advanced researchers of these and related fields. The seminar usually lasts an hour with roughly 35-45 minutes of presentation and the remaining time spent on questions and discussion. This year we introduce an informal gathering immediately after the seminar, with some drinks and snacks/cookies triggering further discussion.
PhD students from EEMCS will receive 1 Graduate School credit from the Graduate School for attending 6 seminars of the series.
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04 March 2024 15:45 till 16:45
[STAT/AP] Richard Gill: A tale of two Lucys"Lucia de Berk, a Dutch nurse, was arrested in 2001, and tried and convicted of serial murder of patients in her care. At a lower court the only hard evidence against her was the result of a probability calculation: the chance that she was present at so many suspicious deaths and collapses in the hospitals where she had worked was 1 in 342 million. During appeal proceedings at a higher court, the prosecution shifted gears and gave the impression that there was now hard evidence that she had killed one baby. Having established that she was a killer and a liar (she claimed innocence) it was not difficult to pin another 9 deaths and collapses on her. No statistics were needed any more. In 2005 the conviction was confirmed by the supreme court. But at the same time, some whistleblowers started getting attention from the media. A long fight for the hearts and minds of the pulbic, and a long fight to have the case reopened (without any new evidence - only new scientific interpretation of existing evidence) began and ended in 2010 with Lucia’s complete exoneration. A number of statisticians played a big role in that fight. The idea that the conviction was purely based on objective scientific evidence was actually an illusion. This needed to be explained to journalists and to the public. And the judiciary needed to be convinced that something had to be done about it.
Lucy Letby, an English nurse, was arrested in 2020 for murder of a large number of babies at a hospital in Chester, UK, in Jan 2015-June 2016. Her trial started in 2022 and took 10 months. She was convicted and given a whole life sentence in 2023.
In my opinion, the similarities between the two cases are horrific. Again there is statistical evidence: a cluster of unexplained bad events, and Lucy was there every time; there is apparently irrefutable scientific evidence for two babies; and just like with Lucia de Berk, there are some weird personal and private writings which can be construed as a confession. For many reasons, the chances of a fair retrial for Lucy Letby are very thin indeed, but I am convinced she is innocent and that her trial was grossly unfair. I will try to convince you, too.
I predict that it will take between 6 and 12 years before she is exonerated."
18 March 2024 15:45 till 16:45
[STAT/AP] Reka Szabo: Stability results via Toom contoursIn this talk I will review Toom's classical result about stability of trajectories of cellular automata. Informally, we say that a cellular automaton is stable if it does not completely lose memory of its initial state when subjected to noise. Using a contour argument Toom gave necessary and sufficient conditions for the cellular automaton to be stable. I will introduce an alternative definition of Toom contours that allows us to extend his method to more general models. I will show how this method can be used to obtain bounds for the critical parameters for certain models, as well as discuss possible applications and limitations of this extension. (Based on joint work with Jan Swart and Cristina Toninelli.)
27 May 2024 15:45 till 16:45
[STAT/AP] Collin Drent: Condition-Based Production for Stochastically Deteriorating Systems: Optimal Policies and LearningProduction systems used in the manufacturing industry degrade due to production and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This degradation behavior, and hence the system's reliability, is affected by the system's production rate. While producing at a higher rate generates more revenue, the system's reliability may also decrease. Production should thus be controlled dynamically to trade-off reliability and revenue accumulation in between maintenance moments. We study this dynamic trade-off for (i) systems where the relation between production and degradation is known as well as (ii) systems where this relation is not known and needs to be learned on-the-fly from condition data. For systems with a known production-degradation relation, we cast the decision problem as a continuous-time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies and we characterize the structure of the optimal interval between scheduled maintenance moments. For systems with an a-priori unknown production-degradation relation, we propose a Bayesian procedure to learn the unknown degradation rate under any production policy from real-time condition data. Numerical studies indicate that on average across a wide range of practical settings (i) condition-based production increases profits by 50% compared to static production, (ii) integrating condition-based production and maintenance interval selection increases profits by 21% compared to a state-of-the-art approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system's production-degradation relation.
Mondays from 4pm to 5 pm
(unless otherwise specified)