Forthcoming Seminars

Mon, 26/04/2004
14:15 		Paul Embrechts (ETH-Zurich) Maths & Finance/Stochastic Analysis Seminar Add to calendar L1
Ruin, time change and operational risk
Mon, 26/04/2004
14:15 		Phillip Griffiths (IAS, Princeton) Geometry and Analysis Seminar Add to calendar L3
Algebraic cycles and singularities of normal functions
Mon, 26/04/2004
17:00 		Scott Spector (Southern Illinois University) Applied Analysis and Mechanics Seminar Add to calendar L1
Cavitation and Configurational Forces in a Nonlinearly Elastic Material
Tue, 27/04/2004
15:00 		Ryan Hayward (Alberta) Combinatorial Theory Seminar Add to calendar L3
Notes on Hex
Tue, 27/04/2004
17:00 		Graham Vincent-Smith (Oxford) Functional Analysis Seminar Add to calendar L3
Abelian subalgebras of the Weyl algebra
Tue, 27/04/2004
17:00 		Dr P M Neumann (Oxford) Algebra Seminar Add to calendar L1
Orbital equations in primitive permutation groups
Wed, 28/04/2004
12:00 		Dietmar Klemm (Milan) String Theory Seminar Add to calendar L3
Holographic Interpretation of Closed Timelike Curves
Thu, 29/04/2004
14:00 		Dr Damien Jenkinson (University of Huddersfield) Computational Mathematics and Applications Add to calendar Rutherford Appleton Laboratory, nr Didcot
Parameterised approximation estimators for mixed noise distributions
Consider approximating a set of discretely defined values $ f_{1}, \ldots , f_{m} $ say at $ x=x_{1}, x_{2}, \ldots, x_{m} $, with a chosen approximating form. Given prior knowledge that noise is present and that some might be outliers, a standard least squares approach based on $ l_{2} $ norm of the error $ \epsilon $ may well provide poor estimates. We instead consider a least squares approach based on a modified measure of the form $ \tilde{\epsilon} = \epsilon (1+c^{2}\epsilon^{2})^{-\frac{1}{2}} $, where $ c $ is a constant to be fixed.
The choice of the constant $ c $ in this estimator has a significant effect on the performance of the estimator both in terms of its algorithmic convergence to a solution and its ability to cope effectively with outliers. Given a prior estimate of the likely standard deviation of the noise in the data, we wish to determine a value of $ c $ such that the estimator behaves like a robust estimator when outliers are present but like a least squares estimator otherwise.
We describe approaches to determining suitable values of $ c $ and illustrate their effectiveness on approximation with polynomial and radial basis functions. We also describe algorithms for computing the estimates based on an iteratively weighted linear least squares scheme.
Thu, 29/04/2004
16:30 		Tiina Roose (Oxford) Differential Equations and Applications Seminar Add to calendar DH Common Room
Root, tumour and frog modelling
Fri, 30/04/2004
15:15 		Angus Macintyre (Edinburgh) Logic Seminar Add to calendar SR1
Comparing the complex and the Zilber-Schanuel exponential
Mon, 03/05/2004
14:15 		Richard Thomas Geometry and Analysis Seminar Add to calendar L3
Stability of algebraic varieties
Mon, 03/05/2004
14:15 		Ma Zhi-Ming Stochastic Analysis Seminar Add to calendar DH 3rd floor SR
An extension of Levy-Khinchine formula in semi-Dirichlet forms setting
The celebrated Levy-Khintchine formula provides us an explicit structure of Levy processes on $R^d$. In this talk I shall present a structure result for quasi-regular semi-Dirichlet forms, i.e., for those semi-Dirichlet forms which are associated with right processes on general state spaces. The result is regarded as an extension of Levy-Khintchine formula in semi-Dirichlet forms setting. It can also be regarded as an extension of Beurling-Deny formula which is up to now available only for symmetric Dirichlet forms.
Mon, 03/05/2004
15:45 		Svante Janson (University of Uppsala) Stochastic Analysis Seminar Add to calendar DH 3rd floor SR
The Brownian snake and random trees
The Brownian snake (with lifetime given by a normalized Brownian excursion) arises as a natural limit when studying random trees. This may be used in both directions, i.e. to obtain asymptotic results for random trees in terms of the Brownian snake, or, conversely, to deduce properties of the Brownian snake from asymptotic properties of random trees. The arguments are based on Aldous' theory of the continuum random tree. I will discuss two such situations: 1. The Wiener index of random trees converges, after suitable scaling, to the integral (=mean position) of the head of the Brownian snake. This enables us to calculate the moments of this integral. 2. A branching random walk on a random tree converges, after suitable scaling, to the Brownian snake, provided the distribution of the increments does not have too large tails. For i.i.d increments Y with mean 0, a necessary and sufficient condition is that the tails are o(y^{-4}); in particular, a finite fourth moment is enough, but weaker moment conditions are not.

For any corrections/updates to these listings, please contact seminars [-at-] maths [dot] ox [dot] ac [dot] uk.

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