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The LiU Seminar Series in Statistics and Mathematical Statistics
Spring 2013
Tuesday, January 15, 3.15 pm, 2013. Seminar in Statistics
Supervised Link Prediction in Dynamic Networks Using Multiple Sources of InformationBerkant Savas, Division of Computational Mathematics, LiU.
Abstract: Link prediction is a fundamental problem in social network analysis and modern-day commercial applications. Most existing research approaches this problem by exploring the topological structure of a social network using only one source of information. However, in many application domains, in addition to the social network of interest, there are a number of auxiliary social networks and/or derived proximity networks available. We will use exponential random graph model (ERGM) to describe the transition probability for the network dynamics and present: (1) a supervised learning framework that can effectively and efficiently learn the dynamics of social networks in the presence of auxiliary networks; (2) a feature design scheme for constructing a rich variety of path-based features using multiple sources, and an effective feature selection strategy based on structured sparsity. Extensive experiments on three real-world collaboration networks show that our model can effectively learn to predict new links using multiple sources, yielding higher prediction accuracy than unsupervised and single source supervised models.
Location: Alan Turing
Tuesday, January 29, 3.15 pm, 2013. Seminar in Mathematical Statistics
Ergodic properties for the conditional distribution of partially observed Markov chainsThomas Kaijser, Mathematical Statistics, LiU.
Abstract: Suppose we want to investigate the properties of a stochastic process using some kind of observation system. As a model for the stochastic process we use a Markov kernel, and similarly for the observation process. (A model of this type is nowadays often called a Hidden Markov Model(HMM) or State Space Model; as a special case we have the so-called Kalman filter.) The interest in such models has been very great the last two (three) decades, and HMMs have e.g. been applied to speech recognition and gene-finding in DNA. The problem, that I have been interested in, is to find conditions which imply ergodic properties for the conditional distribution of the Markov chain, given the observations. (This problem is mainly of theoretical interest, with no immediate practical applications.) My plan is to give a historical overview of this problem and to present some recent results.
Location: Hopningspunkten.
Tuesday, February 12, 3.15 pm, 2013. Seminar in Statistics
Learning models of nonlinear dynamical systemsThomas Schön, Department of Electrical Engineering (ISY), LiU.
Abstract: Learning nonlinear dynamical models typically results in problems lacking analytical solutions. These problems can be attacked using computational methods aiming at approximately solving the problem as good as possible. In this talk we will show how Monte Carlo methods can be used to device powerful algorithms for learning nonlinear dynamical models. More specifically we will make use of sequential Monte Carlo (SMC) methods such as the particle filter and the particle smoother, Markov chain Monte Carlo (MCMC) methods and the combination of MCMC and SMC commonly referred to as particle MCMC. We will study both maximum likelihood solutions and Bayesian solutions. The maximum likelihood estimates are computed using the expectation maximisation algorithm invoking a particle smoother, solving the inherent nonlinear state smoothing problem. The Bayesian solution is obtained using a particle Gibbs (one of the members in the PMCMC family) algorithm with backward simulation. The Wiener model (consisting of a linear dynamical system followed by a static nonlinearity) is used as running example throughout the talk to illustrate the algorithms.
Location: Alan Turing
Tuesday, February 26, 3.15 pm, 2013. Seminar in Mathematical Statistics
TBAHenrik Hult, Mathematical Statistics, KTH.
Abstract: TBA
Location: TBA.
Tuesday, March 12, 3.15 pm, 2013. Seminar in Statistics
TBAShutong Ding, Statistics, Örebro University.
Abstract: TBA
Location: Alan Turing
Tuesday, April 9, 3.15 pm, 2013. Seminar in Statistics
TBAAndriy Andreev, Statistics, Stockholm University.
Abstract: TBA
Location: Alan Turing
Wednesday, April 24, 3.15 pm, 2013. Seminar in Mathematical Statistics
TBATakis Konstantopoulos, Mathematical Statistics, Uppsala University.
Abstract: TBA
Location: TBA.
Tuesday, May 21, 3.15 pm, 2013. Seminar in Mathematical Statistics
TBATatyana Turova, Mathematical Statistics, Lund University.
Abstract: TBA
Location: TBA.
Page responsible: Mattias Villani
Last updated: 2013-01-22
