PROBABILITY, ANALYSIS, AND APPLICATIONS (PAA) WORKSHOP
The deadline for application is 16:59 25th August, 2019
Time-Warping Invariants and the Iterated-Sums Signature of a Time Series
by Prof. Dr. Joscha Diehl
In Data Science, one is often confronted with a time series representing measurements of some quantity of interest. Usually, in a first step, features of the time series need to be extracted. These are numerical quantities that aim to succinctly describe the data. In many applications, these features are required to satisfy some invariance properties. In this lecture series, I focus on time-warping invariants, i.e. the invariance to running the time series at different speeds. The resulting features correspond to certain iterated sums of the increments of the time series. I present these invariant features in a (Hopf) algebraic framework and develop some of their basic properties.
An Introduction to Random Interlacements and related topics
by Prof. Dr. Alexander Drewitz
In these lectures we will give a gentle introduction to the model of random interlacements that has been introduced by Sznitman [Szn10] in 2007.
The model has originally been motivated by questions about the disconnection of discrete cylinders and tori by the trace of simple random walk, as well as by related problems that have been investigated in the theoretical physics literature. Intuitively, random interlacements is a random subset, which appears as the limiting distribution of the trace of simple random walk on a large torus when it runs up to times proportional to the volume. It serves as a model for corrosion and in addition gives rise to interesting and challenging percolation problems through its vacant set (i.e., the complement of random interlacements).
We will start with giving a short review of basic potential theory and then motivate how the model appears as the limiting distribution of simple random walk on the torus. Next, to obtain a better feeling for random interlacements, we will compare it to the well-known model of Bernoulli percolation. We will then survey further important results and tools for the investigation of random interlacements, such as the non-trivial percolation phase transition for its vacant set as well as “decoupling inequalities,” of which stronger and stronger versions have been proven over the last couple of years.
If time admits we will sketch some more recent developments and connections of random interlacements to other fields such as Markovian loop soups, the Gaussian free field, and isomorphism theorems.
An accompanying textbook is [DRS14], a preliminary version of which is available at http://www.mi.uni-koeln.de/~drewitz/SpringerRI.pdf
[DRS14] Alexander Drewitz, Bal´azs R´ath, and Art¨em Sapozhnikov. An introduction to random interlacements. SpringerBriefs in Mathematics. Springer, Cham, 2014.
[Szn10] Alain-Sol Sznitman. Vacant set of random interlacements and percolation. Ann. of Math. (2), 171(3):2039–2087, 2010.
Introduction to Probability Theory and Statistics
by Prof. Dr. Wolfgang König
This is an introduction to the mathematical treatment of the theory of probabilities on base of discrete models and on probabilities with Riemann densities. We start with elementary probability spaces, the concept of independence, random variables, expectations and variances. Then we proceed with the Poisson Limit Theorem, distributions on the integers and their generating functions, the most important distributions and their properties and applications (Poisson, Binomial, Exponential, Normal). Finally, we consider distributions of sums of independent random variables and the weak law of large numbers, fundamental inequalities (e.g., Markov and Chebychev), and the Central Limit Theorem. If time allows, we will give also an introduction to the most basic concepts of mathematical statistics, like estimators, confidence intervals and tests. This theoretical material will be accompanied with examples and exercises. We will follow mainly the monograph “Stochastics — Introduction to Probability and Statistics” by Hans-Otto Georgii (de Gruyter Textbook 2012). We will be delivering 18 copies of this book as gifts to the students, generously supplied by the publisher.
Local Large Deviation and Deviation Principles For The Signal -To-Noise and Interference Ratio Graph Models
by Prof. Kwabena Doku-Amponsah
Mean Field Stochastic Differential Equations
by Prof. Dr. Dirk Becherer
This lecture will focus on an introduction to the theory of Mean Field Stochastic Differential Equations and Applications”.
Continuum Percolation in Random Environment
by Dr. Benedikt Jahnel
In this talk, I will first introduce the basic setting of continuum percolation for Poisson point processes. Motivated by applications in telecommunications, a refined modeling approach will then be discussed, which allows to consider random environments. Finally, I present sufficient conditions on the environment such that nontrivial sub- and supercritical regimes for percolation exist.
Skorokhod embedding solutions to the peacock problem.
By Dr. Antoine-Marie Bogso
The peacock problem is to construct as explicitely as possible a martingale with the same one-dimensional marginals as a given peacock process. The existence of such a martingale is granted by the Kellerer’s celebrated theorem. One interesting approach to solve the peacock problem is to apply Skorokhhod embedding methods. In particular, Azema-Yor and Root Skorokhod embedding algorithms provides solution to the peacock problem in many cases. These solutions as well as some open questions are presented.
 F. Hirs h, C. Profeta, B. Roynette, and M. Yor. Peacocks and associated martingales. Bocconi-Springer, vol 3, 2011.
 Hobson, D. The Skorokhod Embedding Problem and Model-Independent Bounds for Option Prices In Paris-Princeton Lectures on Mathematical Finance 2010, pp. 267-318. Springer, Berlin, Heidelberg, 2011.
 H. G. Kellerer. Markov-Komposition und eine Anwendung auf Martingale. Math. Ann., 198:99 122, 1972.
Multi-population Potts model: Existence of free Energy and Phase Diagram
by Dr. Alex Opoku
In this talk we will discuss multi-population Potts model. This model results from coupling together two or more Potts models. The motivation for studying such class of statistical mechanical models stems from their potential application to discrete choice with social interaction when there are more than two alternatives to choose from.
We will present results on the existence and variational formula for the energy of the model. Some results on the phase diagram of the model will also be discussed.