Regular Seminars
Welcome to CRiSM seminar series!
Seminars take place approximately biweekly in term time, in the Department of Statistics. There will be wine and cheese after the talks in the Statistics Common Room (1.02).
We encourage all postgraduate students (MSc and PhD) to attend this series: it is a great opportunity to know more about current research within the department and outside.
CRiSM seminars 2018/2019 are organised by Dr Ritabrata Dutta.
Thu, Oct 25, '18 
CRiSM SeminarA1.01Speaker: Professor Martyn Plummer, Department of Statistics, Warwick University Abstract: We consider approximate Bayesian model choice for model selection problems that involve models whose Fisher information matrices may fail to be invertible along other competing submodels. Such singular models do not obey the regularity conditions underlying the derivation of Schwarz’s Bayesian information criterion (BIC) and the penalty structure in BIC generally does not reflect the frequentist largesample behavior of their marginal likelihood. While largesample theory for the marginal likelihood of singular models has been developed recently, the resulting approximations depend on the true parameter value and lead to a paradox of circular reasoning. Guided by examples such as determining the number of components of mixture models, the number of factors in latent factor models or the rank in reducedrank regression, we propose a resolution to this paradox and give a practical extension of BIC for singular model selection problems. 

Thu, Nov 8, '18 
CRiSM SeminarA1.01Dr. Martin Tegner, University of Oxford A probabilistic The local volatility model is a celebrated model widely used for pricing and hedging financial derivatives. While the model’s main appeal is its capability of reproducing any given surface of observed option prices—it provides a perfect fit—the essential component of the model is a latent function which can only be unambiguously determined in the limit of infinite data. To (re)construct this function, numerous calibration methods have been suggested involving steps of interpolation and extrapolation, most often of parametric form and with pointestimates as result. We seek to look at the calibration problem in a probabilistic framework with a fully nonparametric approach based on Gaussian process priors. This immediately gives a way of encoding prior believes about the local volatility function and a hypothesis model which is highly flexible whilst being prone to overfitting. Besides providing a method for calibrating a (range of) pointestimate(s), we seek to draw posterior inference on the distribution over local volatility. This to better understand the uncertainty attached with the calibration in particular, and with the model in general. Further, we seek to understand dynamical properties of local volatility by augmenting the hypothesis space with a time dimension. Ideally, this gives us means of inferring predictive distributions not only locally, but also for entire surfaces forward in time.
 

Tue, Nov 20, '18 
CRiSM SeminarA1.01Speaker: Dr. Kayvan Sadeghi, University College London


Thu, Dec 6, '18 
CRiSM SeminarA1.01Speaker: Dr. Carlo Albert, EAWAG, Switzerland 

Thu, Jan 17, '19 
CRiSM SeminarA1.01Dr. Flavio Goncalves, Universidade Federal de Minas Gerais, Brazil. 

Thu, Jan 31, '19 
CRiSM SeminarA1.01
Dr. Sandipan Roy, Department of Mathematical Science, University of Bath Network Heterogeneity and Strength of Connections Abstract: Detecting strength of connection in a network is a fundamental problem in understanding the relationship among individuals. Often it is more important to understand how strongly the two individuals are connected rather than the mere presence/absence of the edge. This paper introduces a new concept of strength of connection in a network through a nonparameteric object called “Grafield”. “Grafield” is a piecewise constant bivariate kernel function that compactly represents the affinity or strength of ties (or interactions) between every pair of vertices in the graph. We estimate the “Grafield” function through a spectral analysis of the Laplacian matrix followed by a hard thresholding (Gavish & Donoho, 2014) of the singular values. Our estimation methodology is valid for asymmetric directed network also. As a by product we get an efficient procedure for edge probability matrix estimation as well. We validate our proposed approach with several synthetic experiments and compare with existing algorithms for edge probability matrix estimation. We also apply our proposed approach to three real datasets understanding the strength of connection in (a) a social messaging network, (b) a network of political parties in US senate and (c) a neural network of neurons and synapses in C. elegans, a type of worm. 

Thu, Feb 14, '19 
CRiSM SeminarA1.01Speaker: Professor Ingo Scholtez, Department of Informatics, University of Zurich, Switzerland 

Thu, Feb 28, '19 
CRiSM SeminarA1.01 

Thu, Mar 14, '19 
CRiSM SeminarA1.01 

Thu, May 2, '19 
CRiSM SeminarA1.01Speaker: Dr. Ben Calderhead, Department of Mathematics, Imperial College London Abstract: QuasiMonte Carlo (QMC) methods for estimating integrals are attractive since the resulting estimators typically converge at a faster rate than pseudorandom Monte Carlo. However, they can be difficult to set up on arbitrary posterior densities within the Bayesian framework, in particular for inverse problems. We introduce a general parallel Markov chain Monte Carlo(MCMC) framework, for which we prove a law of large numbers and a central limit theorem. In that context, nonreversible transitions are investigated. We then extend this approach to the use of adaptive kernels and state conditions, under which ergodicity holds. As a further extension, an importance sampling estimator is derived, for which asymptotic unbiasedness is proven. We consider the use of completely uniformly distributed (CUD) numbers within the above mentioned algorithms, which leads to a general parallel quasiMCMC (QMCMC) methodology. We prove consistency of the resulting estimators and demonstrate numerically that this approach scales close to n^{2} as we increase parallelisation, instead of the usual n^{1} that is typical of standard MCMC algorithms. In practical statistical models we observe multiple orders of magnitude improvement compared with pseudorandom methods. 

Thu, May 30, '19 
CRiSM SeminarA1.01 

Thu, Jun 13, '19 
CRiSM SeminarA1.01 

Thu, Jun 27, '19 
CRiSM SeminarA1.01 