Dr. Pasquale Cirillo (Probability)
Dr. Jakob Söhl (Statistics)


Seminar Series in Probability and Statistics

All seminars are at lunch time: feel free to bring your sandwich!


July 4, 2017: Emilio Cirillo (Università La Sapienza)

When: Tuesday June 27th, 12:45 
Where: TBA


June 27, 2017: Christoph Hofer-Temmel (NLDA)

When: Tuesday June 27th, 12:45 
Where: TBA


June 20, 2017: Matthias Gorny (Université Paris-Sud)

When: Tuesday June 20th, 12:45 
Where: TBA


June 13, 2017: No Seminar

June 9, 2017: Rob Ross
 (TU Delft)

When: Friday June 9th, 12:45 
Where: TBA

Reliability in High Voltage networks – Effective asset management of a strategic infrastructure

The transmission electrical network is the backbone of the electrical grid. It connects large scale power generation to the regional distribution electrical networks and to large customers. Of growing importance are also the interconnections between neighbouring countries and between countries through submarine cable systems.
TenneT is the transmission utility in the Netherlands and a large part of Germany. With a security of supply of 99.9999% and 41 million end-users the challenge is how to preform effective asset management, i.e. how to warrant and make optimal use of the many thousands of objects that together shape the grid. On the one hand billions of euros are invested in the development of grids that embrace sustainable energy. On the other hand a considerable part of the grid is over 30 years of age and still functioning well, but the challenge is to timely detect the need for inspection, refurbishment and replacement. Too early replacement is a waste of public money, but too late replacement may lead to large damage. Asset management aims at doing the right thing at the right time against minimum costs. The underlying evaluation and decision-making are based on expertise and optimized with statistics.
This colloquium will focus on the various issues that asset strategists of electrical power grids face and the methods that are in place or under development in pursue of the maintaining a high reliability and availability of the electric power supply.

June 6, 2017: Yining Chen

When: Tuesday June 6th, 12:45 
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Detecting multiple local extrema via wild binary segmentation

We consider the univariate nonparametric regression problem, where given n observations, the goal is to detect the number and locations of multiple local maxima and minima in the curve. We propose a new approach that combines the ideas of wild binary segmentation (Fryzlewicz, 2014) and mode estimation using isotone regression. We show that our procedure consistently estimates the number of local extrema, and is minimax optimal (up to a logarithmic factor) in estimating the locations of these points. Moreover, we show that the computational complexity of our method is near-optimal (i.e., up to a logarithmic factor, of order n). Finally, we discuss how our approach could be extended to detect other interesting features, such as inflection points.

May 30, 2017: Stéphanie van der Pas (LUMC and Leiden University)

When: Tuesday May 30th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Bayesian community detection

In the stochastic block model, nodes in a graph are partitioned into classes ('communities') and it is assumed that the probability of the presence of an edge between two nodes solely depends on their class labels. We are interested in recovering the class labels, and employ the Bayesian posterior mode for this purpose. We present results on weak consistency (where the fraction of misclassified nodes converges to zero) and strong consistency (where the number of misclassified nodes converges to zero) of the posterior mode, in the 'dense' regime where the probability of an edge occurring between two nodes remains bounded away from zero, and in the 'sparse' regime where this probability does go to zero as the number of nodes increases.

May 23, 2017: Jere Koskela (TU Berlin)

When: Tuesday May 23rd, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Consistency results for Bayesian nonparametric inference from processes with jumps

Consistency can informally be thought of as the ability to infer the true data generating model from a sufficiently large amount of data, and has been regarded as a minimal condition for good inference procedures for several decades. It is also notoriously difficult to verify in the Bayesian nonparametric setting. In recent years, positive results have been established for discretely observed, diffusions under restrictive but verifiable conditions on the prior. I will present an introduction to Bayesian nonparametric inference and posterior consistency, and show how these results for diffusions can be generalised to jump-diffusions under an additional identifiability assumption. Similar arguments will also be shown to yield posterior consistency for a separate class of processes called Lambda-Fleming-Viot processes: inhomogeneous, compactly supported compound Poisson processes arising as models of allele frequencies in population genetics. Identifiability can also be verified rather than assumed for Lambda-Fleming-Viot processes, which results in a tractable set of conditions for posterior consistency that is satisfied e.g. by the popular Dirichlet process mixture model prior.

May 16, 2017: Yong Wang (The University of Auckland)

When: Tuesday May 16th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Mixture-based Nonparametric Density Estimation

In this talk, I will describe a general framework for nonparametric density estimation that uses nonparametric or semiparametric mixture distributions. Similar to kernel-based estimation, the proposed approach uses bandwidth to control the density smoothness, but each density estimate for a fixed bandwidth is determined by likelihood maximization, with bandwidth selection carried out as model selection. This leads to much simpler models than the kernel ones, yet with higher accuracy.
Results of simulation studies and real-world data in both the univariate and the multivariate situation will be given, all suggesting that these mixture-based estimators outperform the kernel-based ones.

May 9, 2017: Hakan Güldas (Leiden University)

When: Tuesday May 9th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Random walks on dynamic configuration models

In this talk, first I will introduce dynamic configuration model which is a dynamic random graph model in discrete time. Then, I will go into details of our results about mixing times of simple random walks on dynamic configuration model. The key property of the dynamic configuration model is that the degrees of the vertices do not change over time. Thanks to this property, the notion of stationary distribution for the random walk on the graph makes sense and mixing occurs although the random walk itself is not Markovian. The results I will give identify the behaviour of mixing times in terms of the proportion of edges that changes at every step of graph dynamics when the number of vertices is large.
Result are based on a joint work with Luca Avena, Remco van der Hofstad and Frank den Hollander.

May 2, 2017: Moritz Schauer (Leiden University)

When: Tuesday May 2nd, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Stochastic monotonicity of Markov processes - A generator approach

We consider stochastic orders on random variables which can be defined in terms of expectations of test functions. Notable examples are the standard stochastic order induced by the increasing functions or the convex order induced by convex functions, capturing the size and spread of random variables. In general, we consider cones of test functions characterized by Φ f ≥ 0 for some linear operator Φ.
Of particular interest are stochastically monotone Markov processes which preserve stochastic order properties in time. The semigroup {S(t): t ≥ 0} of a monotone Markov processes defined by  S(t) f(x) = E [ f(X(t)) | X(0) = x] maps these cones into themselves.
We introduce a new functional analytic technique based on the generator A of the semi-group of a Markov process X(t) and its resolvent to study the property of stochastic monotonicity. We show that the existence of an operator B with positive resolvent such that Φ A - B Φ is a positive operator for a large enough class of functions implies stochastic monotonicity. This establishes a technique for proving stochastic monotonicity and preservation of order for Markov processes that can be applied in a wide range of settings including various orders for diffusion processes with or without boundary conditions and orders for discrete interacting particle systems.
Joint work with Richard C. Kraaij (Ruhr-University of Bochum)

April 25, 2017: Maaneli Derakhshani (Utrecht University)

When: Tuesday April 25th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Hints Toward A Stochastic Hidden-Variables Foundation For Quantum Mechanics

It is well-known that standard quantum theory is plagued by conceptual and technical problems, most notably the quantum measurement problem. The quantum measurement problem indicates that standard quantum theory (whether in non-relativistic or relativistic or quantum-gravitational form) cannot be a fundamental theory of the physical world, and must be replaced by a measurement-problem-free theory of quantum phenomena. Among the viable alternatives to standard quantum theory are nonlocal contextual 'hidden-variable' theories. In this talk, it will be shown that there are tantalizing meta-theoretical hints that the Schroedinger equation and Born-rule interpretation of the wavefunction in standard quantum mechanics have deeper foundations in some nonlocal contextual theory of stochastic hidden-variables. This will be shown by drawing surprising and little-known correspondences between the mathematical structures of Schroedinger's equation and quantum expectation values of physical observables, one the one hand, and the mathematical structures of (1) classical statistical mechanics in the Hamilton-Jacobi representation, (2) the Einstein-Smoluchowski theory of classical Brownian motion, and (3) de Broglie's famous model of a clock particle guided by phase waves, on the other. Finally, it will be suggested that Nelson's stochastic mechanics, and a recent generalization of it proposed by us, constitutes the anticipated theory of stochastic hidden-variables. 

April 18, 2017: Easter Break

April 11, 2017: Dutch Math Congress

April 4, 2017: Ronald Meester (University of Amsterdam)

When: Tuesday April 4th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Why the law of total probability is sometimes not desirable, and how the the theory of belief functions helps to take care of this

We discuss some examples of betting situations in which the law of total probability fails. Since this law follows from the axioms of Kolmogorov and the definition of conditional probability, it follows that a more general theory is necessary. I will formulate more flexible axioms which turn out to characterize belief functions, a well known generalisation of probability measures. Within this theory, conditional belief functions can be defined in various ways, corresponding, roughly, to conditioning on either a necessary truth or a contingent truth. As such, the classical theory is extended and refined at the same time. I will argue that when probability is interpreted epistemically, one should always use belief functions rather than Kolmogorov probability. 

This is joint work with Timber Kerkvliet.

March 28, 2017: Gourab Ray (Cambridge University)

When: Tuesday March 28th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Universality of fluctuation in the dimer model

The dimer model is a very popular model in statistical physics because of its exact solvability properties. I will try to convince you that the fluctuation in the dimer model is universal in the sense that it is more or less independent of the underlying graph and also the topology the graph is embedded in and is given by a form of Gaussian free field.
Joint work with Nathanael Berestycki and Benoit Laslier.

March 21, 2017: Andrew Duncan (University of Sussex)

When: Tuesday March 21st, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Measuring Sample Quality with Diffusions

To improve the efficiency of Monte Carlo estimators, practitioners are turning to biased Markov chain Monte Carlo procedures that trade off asymptotic exactness for computational speed. While a reduction in variance due to more rapid sampling can outweigh the bias introduced, the inexactness creates new challenges for parameter selection. In particular, standard measures of sample quality, such as effective sample size, do not account for asymptotic bias. To address these challenges, we introduce a new computable quality measure based on Stein's method that quantifies the maximum discrepancy between sample and target expectations over a large class of test functions. We demonstrate this tool by comparing exact, biased, and deterministic sample sequences and illustrate applications to hyperparameter selection, convergence rate assessment, and quantifying bias-variance tradeoffs in posterior inference.

March 14, 2017: Kolyan Ray (Leiden University)

When: Tuesday March 14th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Asymptotic equivalence between density estimation and Gaussian white noise revisited

Asymptotic equivalence between two statistical models means that they have the same asymptotic properties with respect to all decision problems with bounded loss. A key result by Nussbaum states that nonparametric density estimation is asymptotically equivalent to a suitable Gaussian shift model, provided that the densities are smooth enough and uniformly bounded away from zero.
We study the case when the latter assumption does not hold and the density is possibly small. We further derive the optimal Le Cam distance between these models, which quantifies how close they are. As an application, we also consider Poisson intensity estimation with low count data.
This is joint work with Johannes Schmidt-Hieber.

March 7, 2017: Frank van der Meulen (TU Delft)

When: Tuesday March 7th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Bayesian estimation for hypo-elliptic diffusions

Suppose X is a discretely observed diffusion process and we wish to sample from the posterior distribution of parameters appearing in either the drift coefficient or the diffusion coefficient. As the likelihood is intractable a common approach is to derive an MCMC algorithm where the missing diffusion paths in between the observations are augmented to the state space. This requires efficient sampling of diffusion bridges. In recent years some results have appeared in the "uniformly elliptic" case, which is characterised by nondegeneracy of the covariance matrix of the noise. The "hypo-elliptic"  case refers to the situation where the covariance matrix of the noise is degenerate and where observations are only made of variables that are not directly forced by white noise. As far as I am aware, not much is known how to sample bridges in this case.
In this talk I will share some recent ideas on extending earlier results with Harry van Zanten (UvA) and Moritz Schauer (Leiden), derived under the assumption of uniformly ellipticity, to this setting.
Joint work with Harry van Zanten (Uva), Moritz Schauer (Leiden) and Omiros Papaspilopoulos (Universitat Pompeu Fabra)

February 28, 2017: No Seminar

February 21, 2017: Pasquale Cirillo (TU Delft) - Cancelled

When: Tuesday February 21st, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Interacting Urn Systems and a Financial Application

February 9 (Extra Thursday!!!), 2017: Gareth Roberts (University of Warwick)

When: Thursday February 9th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Towards not being afraid of the big bad data set

February 7, 2017: Nick Wormald (Monash University)

When: Tuesday February 7th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

A natural infection model

Suppose that individuals are randomly placed points in space according to a Poisson process, and have two states, infected or healthy. Any infected individual passes the infection to any other at distance d according to a Poisson process, whose rate is a function f(d) of d that decreases with d. Any infected individual heals at rate 1. Initially, one individual is infected. An epidemic is said to occur when the infection lasts forever. We investigate conditions on f under which the probability of an epidemic is nonzero. This is joint work with Josep Diaz and Xavier Perez Gimenez.

January 31, 2017: Guido Bacciagaluppi (Utrecht University)

When: Tuesday January 31st, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

Quantum probability and contextuality

In this talk, I shall introduce the generalised theory of probability that arises naturally in quantum mechanics, emphasising its understanding in terms of 'contextuality', and discussing whether and in what sense modelling such phenomena indeed requires going beyond Kolmogorovian probability. 

January 24, 2017: Cancelled

January 17, 2017: Arnaud Le Ny (Université Paris-Est Marne-la-Vallée)

When: Tuesday January 17th, 12:45
Where: TU Delft, Faculty EWI, Mekelweg 4, Room D@ta.

(Very) Persistent Random Walks

In this talk, we shall describe recent works [1] and (maybe) [2] in which we investigate asymptotic properties of one dimensional very Persistent Random Walks (PRW). PRW are correlated walks whose increments are, on the contrary to simple random walks, not i.i.d. but rather dependent in a Markov (finte order) way. They have been widely studied since the mid of last century under different vocables as Goldstein-Kac, correlated or again persistent walks. Due to the extra memory induced by the increments, these random walks are not Marokov proecesses anypore. By very persistent we mean here a model in which even the increments are not Markov, but rather Variable Length Markov Chains whose conditional laws directly depend of the time already spent in the given direction. Equivalently, we are given  two independent sequences of i.i.d. persistence times, in a general possibly non-summable framework that extends previous work of Malduin et al. on Directionnally Recurrent Random Walks [3]. Using an extension of Erickson's criteria [4], we provide a general classification of recurrence vs. Transience in term of drift or tail properties depending on the intial laws, and also identify different regime in the scaling limits for persistent times lying in the bassin of attraction of stable laws.
This is a joint work with P. Cénac (Dijon), B. de Loynes (Rennes) and Y. Offret (Dijon).

[1] P. Cénac, A. Le Ny, B. de Loynes, Y. Offret. Persistent Random Walks I : Recurrence vs. Transience. J. of Theo. Probab. 29, 2016/17.
[2] P. Cénac, A. Le Ny, B. de Loynes, Y. Offret. Persistent Random Walks II : Functional Limit Theorems. Preprint
[3] R. Malduin, M. Monticino, H. von Weisäcker. Directionally Reinforced Random Walks. Adv. In Math. 117, no 2 : 239—252, 1996.
[4] K. Erickson. The Strong Law of Large Numbers when the Mean is Undefined. Trans. Amer. Math. Soc. 185:371--381, 1973.

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