Forthcoming Seminars
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Thu, 08/03/2012 15:00 |
John Duncan |
Representation Theory Seminar  |
L3 |
| In April 2010 Eguchi–Ooguri–Tachikawa observed a fascinating connection between the elliptic genus of a K3 surface and the largest Mathieu group. We will report on joint work with Miranda Cheng and Jeff Harvey that identifies this connection as one component of a system of surprising relationships between a family of finite groups, their representation theory, and automorphic forms of various kinds Mock modular forms, and particularly their shadows, play a key role in the analysis, and we find several of Ramanujan's mock theta functions appearing as McKay–Thompson series arising from the umbral groups. | |||
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Thu, 08/03/2012 16:00 |
Lilian Matthiesen (Bristol) |
Number Theory Seminar |
L3 |
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Thu, 08/03/2012 16:00 |
Alastair Fitt (Brookes University (Oxford)) |
Industrial and Applied Mathematics Seminar |
DH 1st floor SR |
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Thu, 08/03/2012 17:00 |
Logic Seminar |
L3 | |
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Fri, 09/03/2012 00:00 |
Mathematical Biology and Ecology Seminar |
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Fri, 09/03/2012 09:45 |
Raoof (Loughborough University) |
Industrial and Interdisciplinary Workshops |
DH 1st floor SR |
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Fri, 09/03/2012 11:00 |
Various |
OCCAM Special Seminar |
OCCAM Common Room (RI2.28) |
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Fri, 09/03/2012 14:15 |
Marcel Nutz (Columbia) |
Nomura Seminar |
DH 1st floor SR |
| We provide a general construction of time-consistent sublinear expectations on the space of continuous paths. In particular, we construct the conditional G-expectation of a Borel-measurable (rather than quasi-continuous) random variable. | |||
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Fri, 09/03/2012 14:30 |
Prof. Leonard A. Smith (London School of Economics and Pembroke College) |
Mathematical Geoscience Seminar |
DH 3rd floor SR |
| Probability does not exist. At least no more so than "mass" "spin" or "charm" exist. Yet probability forecasts are common, and there are fine reasons for deprecating point forecasts, as they require an unscientific certainty in exactly what the future holds. What roles do our physical understanding and laws of physics play in the construction of probability forecasts to support of decision making and science-based policy? Will probability forecasting more likely accelerate or retard the advancement of our scientific understanding? Model-based probability forecasts can vary significantly with alterations in the method of data assimilation, ensemble formation, ensemble interpretation, and forecast evaluation, not to mention questions of model structure, parameter selection and the available forecast-outcome archive. The role of each of these aspects of forecasting, in the context of interpreting the forecast as a real-world probability, is considered and contrasted in the cases of weather forecasting, climate forecasting, and economic forecasting. The notion of what makes a probability forecast "good" will be discussed, including the goals of "sharpness given calibration" and "value". For a probability forecast to be decision-relevant as such, it must be reasonably interpreted as a basis for rational action through the reflection of the probability of the outcomes forecast. This rather obvious sounding requirement proves to be the source of major discomfort as the distinct roles of uncertainty (imprecision) and error (structural mathematical "misspecification") are clarified. Probabilistic forecasts can be of value to decision makers even when it is irrational to interpret them as probability forecasts. A similar statement, of course, can be said for point forecasts, or for spin. In this context we explore the question: do decision-relevant probability forecasts exist? | |||
