# Difference between revisions of "Past Probability Seminars Spring 2020"

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We study the large deviation principle for the empirical measure of general irreducible Markov chains in the tao topology for a broad class of initial distributions. The roles of several rate functions, including the rate function based on the convergence parameter of the transform kernel and the Donsker-Varadhan rate function, are clarified. | We study the large deviation principle for the empirical measure of general irreducible Markov chains in the tao topology for a broad class of initial distributions. The roles of several rate functions, including the rate function based on the convergence parameter of the transform kernel and the Donsker-Varadhan rate function, are clarified. | ||

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+ | == Thursday, November 10, TBA, TBA == | ||

+ | Title: TBA | ||

== Thursday, November 17, [https://sites.google.com/a/wisc.edu/louisfan/home Louis Fan], [https://www.math.wisc.edu/ UW-Madison] == | == Thursday, November 17, [https://sites.google.com/a/wisc.edu/louisfan/home Louis Fan], [https://www.math.wisc.edu/ UW-Madison] == |

## Revision as of 14:20, 22 October 2016

# Fall 2016

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

**
If you would like to sign up for the email list to receive seminar announcements then please send an email to join-probsem@lists.wisc.edu.
**

## Thursday, September 8, Daniele Cappelletti, UW-Madison

Title: **Reaction networks: comparison between deterministic and stochastic models**

Abstract: Mathematical models for chemical reaction networks are widely used in biochemistry, as well as in other fields. The original aim of the models is to predict the dynamics of a collection of reactants that undergo chemical transformations. There exist two standard modeling regimes: a deterministic and a stochastic one. These regimes are chosen case by case in accordance to what is believed to be more appropriate. It is natural to wonder whether the dynamics of the two different models are linked, and whether properties of one model can shed light on the behavior of the other one. Some connections between the two modelling regimes have been known for forty years, and new ones have been pointed out recently. However, many open questions remain, and the issue is still largely unexplored.

## Friday, September 16, 11 am Elena Kosygina, Baruch College and the CUNY Graduate Center

** Please note the unusual day and time **

The talk will be in Van Vleck 910 as usual.

Title: **Homogenization of viscous Hamilton-Jacobi equations: a remark and an application.**

Abstract: It has been pointed out in the seminal work of P.-L. Lions, G. Papanicolaou, and S.R.S. Varadhan that for the first order Hamilton-Jacobi (HJ) equation, homogenization starting with affine initial data should imply homogenization for general uniformly continuous initial data. The argument utilized the properties of the HJ semi-group, in particular, the finite speed of propagation. The last property is lost for viscous HJ equations. We remark that the above mentioned implication holds under natural conditions for both viscous and non-viscous Hamilton-Jacobi equations. As an application of our result, we show homogenization in a stationary ergodic setting for a special class of viscous HJ equations with a non-convex Hamiltonian in one space dimension. This is a joint work with Andrea Davini, Sapienza Università di Roma.

## Thursday, September 22, Philip Matchett Wood, UW-Madison

Title: **Low-degree factors of random polynomials**

Abstract: We study the probability that a monic polynomial with integer coefficients has a low-degree factor over the integers. It is known that certain models are very likely to produce random polynomials that are irreducible, and our project can be viewed as part of a general program of testing whether this is a universal behavior exhibited by many random polynomial models. Interestingly, though the question comes from algebra and number theory, we primarily use tools from combinatorics, including additive combinatorics, and probability theory. We prove for a variety of models that it is very unlikely for a random polynomial with integer coefficients to have a low-degree factor—suggesting that this is, in fact, a universal behavior. For example, we show that the characteristic polynomial of random matrix with independent +1 or −1 entries is very unlikely to have a factor of degree up to [math]n^{1/2-\epsilon}[/math]. Joint work with Sean O’Rourke. The talk will also discuss joint work with UW-Madison undergraduates Christian Borst, Evan Boyd, Claire Brekken, and Samantha Solberg, who were supported by NSF grant DMS-1301690 and co-supervised by Melanie Matchett Wood.

## Thursday, September 29, Joseph Najnudel, University of Cincinnati

Title: **On the maximum of the characteristic polynomial of the Circular Beta Ensemble**

In this talk, we present our result on the extremal values of (the logarithm of) the characteristic polynomial of a random unitary matrix whose spectrum is distributed according to the Circular Beta Ensemble. Using different techniques, it gives an improvement and a generalization of the previous recent results by Arguin, Belius, Bourgade on the one hand, and Paquette, Zeitouni on the other hand. They recently treated the CUE case, which corresponds to beta equal to 2.

## Thursday, October 6, No Seminar

## Thursday, October 13, No Seminar due to Midwest Probability Colloquium

For details, see Midwest Probability Colloquium.

## Thursday, October 20, Amol Aggarwal, Harvard

Title: Current Fluctuations of the Stationary ASEP and Six-Vertex Model

Abstract: We consider the following three models from statistical mechanics: the asymmetric simple exclusion process, the stochastic six-vertex model, and the ferroelectric symmetric six-vertex model. It had been predicted by the physics communities for some time that the limiting behavior for these models, run under certain classes of translation-invariant (stationary) boundary data, are governed by the large-time statistics of the stationary Kardar-Parisi-Zhang (KPZ) equation. The purpose of this talk is to explain these predictions in more detail and survey some of our recent work that verifies them.

## Thursday, October 27, Hung Tran, UW-Madison

Title: **Homogenization of non-convex Hamilton-Jacobi equations**

Abstract: I will describe why it is hard to do homogenization for non-convex Hamilton-Jacobi equations and explain some recent results in this direction. I will also make a very brief connection to first passage percolation and address some challenging questions which appear in both directions. This is based on joint work with Qian and Yu.

## Thursday, November 3, Alejandro deAcosta, Case-Western Reserve

Title: **Large deviations for irreducible Markov chains with general state space**

Abstract: We study the large deviation principle for the empirical measure of general irreducible Markov chains in the tao topology for a broad class of initial distributions. The roles of several rate functions, including the rate function based on the convergence parameter of the transform kernel and the Donsker-Varadhan rate function, are clarified.

## Thursday, November 10, TBA, TBA

Title: TBA

## Thursday, November 17, Louis Fan, UW-Madison

Title: TBA

## Thursday, November 24, No Seminar due to Thanksgiving

## Thursday, December 1, TBA, TBA

Title: TBA

## Thursday, December 8, TBA, TBA

Title: TBA

## Thursday, December 15, TBA, TBA

Title: TBA