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Mon 7 Jan, '13
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SF@W Seminars
A1.01
Tue 8 Jan, '13
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Professor Talks
B1.01 (Maths)
Tue 8 Jan, '13
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Interview Candidates Lunch
C0.06 Stats Common Rm
Tue 8 Jan, '13
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Young Researchers Meeting
C0.06 Stats Common Rm
Wed 9 Jan, '13
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Department Council Mtg
C0.06 Stats Common Rm
Wed 9 Jan, '13
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Staff Lunch
C0.06 Stats Common Rm
Wed 9 Jan, '13
-
Midlands Probability Theory Seminar
B3.02 (Maths)
Thu 10 Jan, '13
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Professor Talks
A1.01
Fri 11 Jan, '13
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APTS Executive Committee Mtg
B1.16 (Maths)
Fri 11 Jan, '13
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Algorithms & Computationally Intensive Inference Seminars
A1.01
Mon 14 Jan, '13
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CRiSM Research Fellow Presentations
A1.01
Mon 14 Jan, '13
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SF@W Seminars
A1.01
Tue 15 Jan, '13
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Young Researchers Meeting
C0.06 Stats Common Rm
Wed 16 Jan, '13
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Teaching Committee
C1.06
Thu 17 Jan, '13
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Probability Reading Group
C1.06
Thu 17 Jan, '13
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CRiSM Seminar - Anastasia Papavasiliou (Warwick)
A1.01

Dr Anastasia Papavasiliou (University of Warwick)

Statistical Inference for differential equations driven by rough paths

Differential equations driven by rough paths (RDEs for short) generalize SDEs by allowing the equation to be driven by any type of noise and not just Brownian motion. As such, they are a very flexible modelling tool for randomly evolving dynamical systems. So far, however, they have been ignored by the statistics community, in my opinion for the two followin reasons: (i) the abstract theory of rough paths is still very young and under development, which makes it very hard to penetrate; (ii) there are no statistical tools available. In this talk, I will give an introduction to the theory and I will also discuss how to approach the problem of statistical inference given a discretely observed solution to an RDE.

Fri 18 Jan, '13
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Warwick Statistics Research Fellow Presentations
C1.06
Fri 18 Jan, '13
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Algorithms & Computationally Intensive Inference Seminars
A1.01
Mon 21 Jan, '13
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Warwick Statistics Research Fellow Presentations
A1.01
Mon 21 Jan, '13
-
SF@W Seminars
A1.01
Tue 22 Jan, '13
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Young Researchers Meeting
C0.06 Stats Common Rm
Thu 24 Jan, '13
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Probability Reading Group
C1.06
Thu 24 Jan, '13
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CRiSM Seminar - Evangelos Evangelou
A1.01

Evangelos Evangelou (University of Bath)

Spatial sampling design under cost constraints in the presence of sampling errors

A sampling design scheme for spatial models for the prediction of the underlying Gaussian random field will be presented. The optimality criterion is the maximisation of the information about the random field contained in the sample. The model discussed departs from the typical spatial model by assuming measurement error in the observations, varying from location to location, while interest lies in prediction without the error term. In this case multiple samples need to be taken from each sampling location in order to reduce the measurement error. To that end, a hybrid algorithm which combines simulated annealing nested within an exchange algorithm will be presented for obtaining the optimal sampling design. Consideration is made with regards to optimal sampling under budget constraints accounting for initialisation and sampling costs.

Joint work with Zhengyuan Zhu (Iowa State)

Fri 25 Jan, '13
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Algorithms & Computationally Intensive Inference Seminars
A1.01
Mon 28 Jan, '13
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SF@W Seminars
A1.01
Tue 29 Jan, '13
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Young Researchers Meeting
C0.06 Stats Common Rm
Wed 30 Jan, '13
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Admissions Interviews
C0.06 Stats Common Rm & A1.01
Wed 30 Jan, '13
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UG SSLC
C1.06
Thu 31 Jan, '13
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Probability Reading Group
C1.06
Thu 31 Jan, '13
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CRiSM Seminar - Catriona Queen
A1.01

Catriona Queen (The Open University)

A graphical dynamic approach to forecasting flows in road traffic networks

Traffic flow data are routinely collected for many networks worldwide. These invariably large data sets can be used as part of a traffic management system, for which good traffic flow forecasting models are crucial. While statistical flow forecasting models usually base their forecasts on flow data alone, data for other traffic variables are also routinely collected. This talk considers how cubic splines can be used to incorporate information from these extra variables to enhance flow forecasts. The talk also introduces a new type of chain graph model for forecasting traffic flows. The models are applied to the problem of forecasting multivariate road traffic flows at the intersection of three busy motorways near Manchester, UK.

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