# Past Probability Seminars Spring 2020

## Fall 2013

Thursdays in 901 Van Vleck Hall at 2:25 PM, unless otherwise noted.

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## Thursday, September 12, Tom Kurtz, UW-Madison

Title: ** Particle representations for SPDEs with boundary conditions **

Abstract: Stochastic partial differential equations frequently arise as limits of finite systems of weighted interacting particles. For a variety of purposes, it is useful to keep the particles in the limit obtaining an infinite exchangeable system of stochastic differential equations for the particle locations and weights. The corresponding de Finetti measure then gives the solution of the SPDE. These representations frequently simplify existence, uniqueness and convergence results. Following some discussion of general approaches to SPDEs, the talk will focus on situations where the particle locations are given by an iid family of diffusion processes, and the weights are chosen to obtain a nonlinear driving term and to match given boundary conditions for the SPDE. (Recent results are joint work with Dan Crisan.)

## Thursday, September 26, David F. Anderson, UW-Madison

Title: Stochastic analysis of biochemical reaction networks with absolute concentration robustness

Abstract: It has recently been shown that structural conditions on the reaction network, rather than a 'fine-tuning' of system parameters, often suffice to impart "absolute concentration robustness" on a wide class of biologically relevant, deterministically modeled mass-action systems [Shinar and Feinberg, Science, 2010]. Many biochemical networks, however, operate on a scale insufficient to justify the assumptions of the deterministic mass-action model, which raises the question of whether the long-term dynamics of the systems are being accurately captured when the deterministic model predicts stability. I will discuss recent results that show that fundamentally different conclusions about the long-term behavior of such systems are reached if the systems are instead modeled with stochastic dynamics and a discrete state space. Specifically we characterize a large class of models which exhibit convergence to a positive robust equilibrium in the deterministic setting, whereas trajectories of the corresponding stochastic models are necessarily absorbed by a set of states that reside on the boundary of the state space. The results are proved with a combination of methods from stochastic processes and chemical reaction network theory.

## Thursday, October 3, Lam Si Tung Ho, UW-Madison

Title: Asymptotic theory of Ornstein-Uhlenbeck tree models

Abstract: Tree models arise in evolutionary biology when sampling biological species, which are related to each other according to a phylogenetic tree. When observations are modeled using an Ornstein-Uhlenbeck (OU) process along the tree, the autocorrelation between two tips decreases exponentially with their tree distance. Under these models, tips represent biological species and the OU process parameters represent the strength and direction of natural selection. For the mean, we show that if the heights of the trees are bounded, then it is not microergodic: no estimator can ever be consistent for this parameter. On the other hand, if the heights of the trees converge to infinity, then the MLE of the mean is consistent and we establish a phase transition on the behavior of its variance. For covariance parameters, we give a general sufficient condition ensuring microergodicity. We also provide a [math]\sqrt{n}[/math]-consistent estimators for them under some mild conditions.

## Thursday, October 10, NO SEMINAR

Midwest Probability Colloquium

## Tuesday, October 15, 4pm, Van Vleck B239, Distinguished Lecture Series in Mathematics: Alexei Borodin, MIT

Please note the non-standard time and day.

Title: **Integrable Probability I**

Abstract: The goal of the talks is to describe the emerging field of integrable probability, whose goal is to identify and analyze exactly solvable probabilistic models. The models and results are often easy to describe, yet difficult to find, and they carry essential information about broad universality classes of stochastic processes.

## Wednesday October 16, 2:30pm, Van Vleck B139, Distinguished Lecture Series in Mathematics: Alexei Borodin, MIT

Please note the non-standard time and day.

Title: **Integrable Probability II**

Abstract: The goal of the talks is to describe the emerging field of integrable probability, whose goal is to identify and analyze exactly solvable probabilistic models. The models and results are often easy to describe, yet difficult to find, and they carry essential information about broad universality classes of stochastic processes.

## Tuesday, October 22, 4pm, Van Vleck 901, Anton Wakolbinger, Goethe Universität Frankfurt

Please note the non-standard time and day, **and the recently revised time and room.**

Title: **The time to fixation of a strongly beneficial mutant in a structured population**

Abstract: We discuss a system that describes the evolution of the vector of relative frequencies of a beneficial allele in d colonies, starting in (0,...,0) and ending in (1,...,1). Its diffusion part consists of Wright-Fisher noises in all the components that model the random reproduction, its drift part consists of a linear interaction term coming from the gene flow between the colonies, together with a logistic growth term due to the selective advantage of the allele, and a term which makes the entrance from (0,...,0) possible. It turns out that there are d extremal ones among the solutions of the system, each of them corresponding to one colony in which the beneficial mutant originally appears. We then focus on the fixation time in the limit of a large selection coefficient, and explain how its asymptotic distribution can be analysed in terms of the so called ancestral selection graph. This is joint work with Andreas Greven, Peter Pfaffelhuber and Cornelia Pokalyuk.

## Thursday, October 24, Ke Wang, IMA

Title: Random weighted projections, random quadratic forms and random eigenvectors

Abstract: We start with a simple, yet useful, concentration inequality concerning random weighted projections in high dimensional spaces. The inequality is used to prove a general concentration inequality for random quadratic forms. In another application, we show the infinity norm of most unit eigenvectors of Hermitian random matrices with bounded entries is [math]O(\sqrt{\log n/n})[/math]. This is joint work with Van Vu.

## Thursday, November 14, Miklos Racz, UC Berkeley

Title: TBA

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## Thursday, November 21, Amarjit Budhiraja, UNC-Chapel Hill

Title: TBA

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## Thursday, December 5, Scott Hottovy, UW-Madison

Title: TBA

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## Thursday, December 12, Nikos Zygouras, University of Warwick

Title: TBA

Abstract: