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

(→Thursday, February 22, 2017, Garvesh Raskutti UW-Madison Stats and WID) |
(→Thursday, February 8, 2017, Jon Peterson, Purdue) |
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== Thursday, February 8, 2017, [http://www.math.purdue.edu/~peterson/ Jon Peterson], [http://www.math.purdue.edu/ Purdue] == | == Thursday, February 8, 2017, [http://www.math.purdue.edu/~peterson/ Jon Peterson], [http://www.math.purdue.edu/ Purdue] == | ||

− | Title: | + | Title: '''Quantitative CLTs for random walks in random environments''' |

− | Abstract: | + | Abstract:The classical central limit theorem (CLT) states that for sums of a large number of i.i.d. random variables with finite variance, the distribution of the rescaled sum is approximately Gaussian. However, the statement of the central limit theorem doesn't give any quantitative error estimates for this approximation. Under slightly stronger moment assumptions, quantitative bounds for the CLT are given by the Berry-Esseen estimates. In this talk we will consider similar questions for CLTs for random walks in random environments (RWRE). That is, for certain models of RWRE it is known that the position of the random walk has a Gaussian limiting distribution, and we obtain quantitative error estimates on the rate of convergence to the Gaussian distribution for such RWRE. This talk is based on joint works with Sungwon Ahn and Xiaoqin Guo. |

== Thursday, February 15, 2017, TBA== | == Thursday, February 15, 2017, TBA== |

## Revision as of 10:49, 17 January 2018

# Spring 2018

**Thursdays in 901 Van Vleck Hall at 2:25 PM**, unless otherwise noted.
**We usually end for questions at 3:15 PM.**

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, February 1, 2017, Hoi Nguyen, OSU

Title: TBA

Abstract: TBA

## Thursday, February 8, 2017, Jon Peterson, Purdue

Title: **Quantitative CLTs for random walks in random environments**

Abstract:The classical central limit theorem (CLT) states that for sums of a large number of i.i.d. random variables with finite variance, the distribution of the rescaled sum is approximately Gaussian. However, the statement of the central limit theorem doesn't give any quantitative error estimates for this approximation. Under slightly stronger moment assumptions, quantitative bounds for the CLT are given by the Berry-Esseen estimates. In this talk we will consider similar questions for CLTs for random walks in random environments (RWRE). That is, for certain models of RWRE it is known that the position of the random walk has a Gaussian limiting distribution, and we obtain quantitative error estimates on the rate of convergence to the Gaussian distribution for such RWRE. This talk is based on joint works with Sungwon Ahn and Xiaoqin Guo.

## Thursday, February 15, 2017, TBA

## Thursday, February 22, 2017, Garvesh Raskutti UW-Madison Stats and WID

Title: TBA