Note that all attendees are welcome to attend the key skills training sessions.
The only private UTOPIAE sessions are 2pm–5pm on Tuesday and 1pm–4pm on Wednesday.
On Tuesday attendees who are not part of UTOPIAE are welcome to present short talks on their work in a parallel session: if you would like to do this, please tick the option at registration and we will contact you for a talk title.
Mon 02 Jul| 09:00-11:00 | |
|---|---|
| Introduction to Bayesian statistics | Georgios Karagiannis, University of Durham |
| We introduce basic concepts of Bayesian theory. We introduce the Bayesian paradigm, and discuss the subjective interpretation of probability. We study methods to incorporate prior information in the Bayesian statistical analysis. From a decision theory perspective, we study parametric and predictive inference, and particularly the design of point estimators, credible sets, and hypothesis tests. Finally, we discuss how the these concepts can be applied to build complex Bayesian models able to address realistic problems. | |
| 11:00-13:00 | |
| Introduction to the theory of imprecise probability | Erik Quaeghebeur, TU, Delft |
| The theory of imprecise probability is a generalization of classical ‘precise’ probability theory that allows modeling imprecision and indecision. This is a practical advantage in situations where a unique precise uncertainty model cannot be justified. This arises, for example, when there is a relatively small amount of data available to learn the uncertainty model or when the model’s structure cannot be defined uniquely. The tools the theory provides make it possible to draw conclusions and make decisions that correctly reflect the limited information or knowledge available for the uncertainty modeling task. This extra expressivity however often implies a higher computational burden. The goal of this lecture is to primarily give you the necessary knowledge to be able to read literature that makes use of the theory of imprecise probability. A secondary goal is to provide the insight needed to use imprecise probabilities in your own research. To achieve the goals, we will present the essential concepts and techniques from the theory, as well as give a less in-depth overview of the various specific uncertainty models used. Throughout, examples will be used to make things concrete. We build on the assumed basic knowledge of classical probability theory, for which parts of the “Introduction to Bayesian statistics” lecture provides a refresher, and prepare the way for many of the subsequent lectures of the School. |
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| 13:00-13:40 | |
| Lunch | |
| 13:40-15:40 | |
| Statistical modelling with imprecise probabilities | Thomas Augustin, University of Munich |
| The presentation gives a first introduction into statistical modelling with imprecision. The first part considers some typical applications in the context of model imprecision. We first show how imprecise probabilities provide a natural superstructure upon (frequentist) robust statistics. Then we look at the use of imprecise priors in Generalized Bayesian inference, where we discuss the proper handling of near prior ignorance as well as of prior-data conflict. The second part focuses on data imprecision, i.e. situations where data are not observed in the resolution originally intended. Following recent work by Couso \& Dubois, we distinguish between ontic and epistemic data imprecision (precise observation of something genuinely imprecise versus imprecise observation of something genuinely precise) and discuss for both situations first approaches for modelling coarse and missing data with imprecise probabilities. |
|
| 16:15- | |
| Tour of Durham Castle | |
| 09:00-11:00 | |
| Markov chains with imprecise probabilities | Gert de Cooman, Ghent University |
| Abstract to follow | |
| 11:00-13:00 | |
| Introduction to reliability theory | Lisa Jackson, Loughborough University and Frank Coolen, University of Durham |
| In this lecture we introduce basic concepts of reliability theory, with focus on system reliability. We discuss and illustrate reliability quantification for a range of scenarios, including dependent failures, phased missions and competing risks. We discuss statistical methods for reliability inferences, including imprecise probability approaches. Finally, we will briefly discuss several challenges for research, related to resilience, lack of information and upscaling of theory and methods to large real world applications. | |
| 13:00-14:00 | |
| Lunch | |
| 14:00-17:00 | |
| Afternoon ESR activity (UTOPIAE personnel only) | |
| Non-UTOPIAE participant short talks | |
| 09:00-11:00 | |
| Sampling from complex probability distributions | Louis Aslett, University of Durham |
| The need to sample from complex probability distributions arises in a multitude of settings, from simulation of an engineering system through to Bayesian posterior inference. In this lecture we will introduce standard Monte Carlo methods and continue through to modern Markov Chain Monte Carlo (MCMC) techniques, carefully building up a little introductory level theory, several modern methodologies and practical implementation. The context will be kept as general as possible, focusing on an arbitrary target distribution of interest, enabling the techniques learned to be applied in any setting where a probability distribution can be specified up to a constant of proportionality. | |
| 11:00-12:00 | |
| Key skills training: Reviewing papers | Jochen Einbeck, University of Durham |
| This key skills session discusses several aspects of scholarly peer review, with focus on the referee’s role in this central element of scientific publishing. The main part of this session will be arranged in form of a workshop where participants identify examples of good and bad practice from a set of case studies. | |
| 12:00-13:00 | |
| Lunch | |
| 13:00-16:00 | |
| Project working group presentations | ESRs, UTOPIAE |
| 16:00-17:00 | |
| Public Lecture: Randomness | Gert de Cooman, Ghent University |
| The randomness of a sequence of numbers can be defined in many ways. The talk begins with a short survey of the most common definitions of randomness and their relationships, and then focuses on a powerful and intuitive martingale-theoretic definition first suggested by Ville, and further refined by Schnorr and Levin. It essentially requires that there should be no (in some way computationally achievable) strategy for gambling on the successive outcomes in the sequence that allows a player, Skeptic, to become infinitely rich without borrowing. Interestingly, this betting approach allows for a generalisation towards interval (or imprecise) probabilities. As is often the case with the mathematics of imprecise probabilities, this allows for new ideas and structures to emerge, and takes us to a new vantage point from where it becomes easier to appreciate the subtleties and intricacies associated with the precise limit case where intervals reduce to numbers. | |
| 09:00-11:00 | |
| Engineering pathways for quantifying inconsistent information | Michael Beer, Leibniz Universität Hannover |
| In engineering practice data and information are normally not available in an ideal form for mathematical quantification with with probabilistic models. In those cases imprecise probabilities offer a framework to remedy the problem. However, depending on the specific problem, there is still a large range of options on how to put up the model. In some cases, we may have strong evidence for the type of distribution, and vagueness exists only on the probabilistic model parameter values. In other cases, we may have conflicting expert assessments on distribution types and some data of only poor quality. Occasionally, we may only have a rough idea about the range of structural or environmental parameters associated with a credibility expressed as subjective probability. In each particular case the analyst needs to decide how to establish the model using the rich framework of imprecise probabilities, matching the technical context of the problem at hand. In this presentation we will discuss practical engineering pathways for solving this problem. | |
| 11:00-13:00 | |
| Simulation methods for the analysis of complex systems | Edoardo Patelli, University of Liverpool |
| Analysis of systems is traditionally performed adopting different tools reliability block diagrams, fault tree and success tree methods, failure mode and effect analysis, etc… These traditional approaches are difficult or impossible to apply to large complex systems due to the complex and tedious calculations for finding minimal path sets and cut sets. Instead, simulation approaches can be used to investigate large and complex systems and for obtaining numerical solutions where analytical solutions are not available. In particular, simulation methods allow to consider explicitly the effect of uncertainty and imprecision on the system under investigation providing a powerful tool for risk analysis which allows better decision making under uncertainty. A simple and generally applicable simulation approach, enhanced for multi-state systems of any topology is presented. Each component is defined as a Semi-Markov stochastic process and via discrete-event simulation. The principles of flow conservation are invoked to determine flow across the system for every performance level change of its components using the interior-point algorithm. This eliminates the need for cut-set definition and overcomes the limitations of existing techniques. The methodology can also be exploited to account for effects of transmission efficiency and loading restrictions of components on system reliability and performance. The approach is simple and generally applicable to systems, including those with limited maintenance teams, reconfiguration requirements, and multiple commodity flows In addition, efficient simulation approaches will be presented to estimate the reliability of systems based on survival signature. The simulation approaches are generally applicable to any system configuration and allow to consider different representation of the uncertainties (i.e. distribution, intervals, fuzzy etc) and system multi-state components (i.e. repairable components). |
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| 13:00-14:00 | |
| Lunch | |
| 14:00-17:00 | |
| Key skills training: 1. Publish successfully 2. Careers advice | Michael Beer, Leibniz Universität Hannover |
| Publish successfully: The presentation will highlight some key issues to take of when preparing journal papers. Advice will be provided on how to select the right journal for the paper, how to define the right paper type, and how to work out a research paper with emphasis on the key items that make it a research paper. In addition to these basic items, information will be provided on how to address reviewer comments, how to understand editorial decisions, and how to deal with conflicting information and problems. In overall, the information provided targets at increasing the chance for success when submitting journal papers. This presentation is based on own experience as an author, reviewer and handling and associate editor for several international journals. Careers advice: Going for an academic career means to set out for a dynamic pathway with often unexpected obstacles and restrictions and also chances. Getting prepared early helps to reduce uncertainties and to increase chances significantly. The presentation will highlight a number of facts that are crucial for success but are often not considered or considered too late. It will be explained how to develop an impactful CV as a basis for applying for academic jobs worldwide. It will also be explained how applications are assessed, and how to prepare them to make the most important items clear to the panels and referees. This presentation is based on own experience in different academic systems and on experience from assessing applications and writing and assessing reference letters. |
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| During this time the UTOPIAE Network meeting will take place - UTOPIAE supervisors and managers | |
| 18:30- | |
| UTOPIAE Dinner | Hatfield College |
| 09:00-11:00 | |
| Reduction approaches for the Bayesian inference of model parameter: methods and examples | Olivier Le Maître, CNRS Pietro Congedo, INRIA |
| In this lecture, I will start by introducing the basic ideas of the Bayesian inference of model parameters, and the Markov Chain Monte Carlo (MCMC) method for the sampling of the posterior distribution. This introduction will be used to highlight the computational complexity of the Bayesian procedure. In the following of the lecture, I will discuss several reduction methods aiming at mitigating this computational complexity. The reduction methods will concern the different elements of the Bayesian inference, namely, the prediction model, the observations, and the hyper-parameters of the prior. |
|
| 11:00-13:00 | |
| Analysis and model validation starting with experimental measurements | Olivier Chazot and Thierry Magin, von Karman Institute |
| The lecture presents, in a first part the conception of experiments for the investigations of aerothermodynamic phenomena in high enthalpy facilities and plasma wind tunnels. It develops the framework in which an experiment is relevant for representing a real flight situation and underlines its limitations. The second part focus on the experimental measurements with their techniques and their related data processing tools. It exposes the databases that could be generated with the definition of the corresponding basic uncertainty margins. |
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