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

(→Thursday, February 11, Sam Stechmann, UW-Madison) |
(→Thursday, March 19, Mark Huber, Claremont McKenna Math) |
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== Thursday, March 19, [http://www.cmc.edu/pages/faculty/MHuber/ Mark Huber], [http://www.cmc.edu/math/ Claremont McKenna Math] == | == Thursday, March 19, [http://www.cmc.edu/pages/faculty/MHuber/ Mark Huber], [http://www.cmc.edu/math/ Claremont McKenna Math] == | ||

− | Title: | + | Title: Understanding relative error in Monte Carlo simulations |

− | Abstract: | + | Abstract: The problem of estimating the probability $p$ of heads on an unfair coin has been around for centuries, and has inspired numerous advances in probability such as the Strong Law of Large Numbers and the Central Limit Theorem. In this talk, I'll consider a new twist: given an estimate $\hat p$, suppose we want to understand the behavior of the relative error $(\hat p - p)/p$. In classic estimators, the values that the relative error can take on depends on the value of $p$. I will present a new estimate with the remarkable property that the distribution of the relative error does not depend in any way on the value of $p$. Moreover, this new estimate is very fast: it takes a number of coin flips that is very close to the theoretical minimum. Time permitting, I will also discuss new ways to use concentration results for estimating the mean of random variables where normal approximations do not apply. |

== Thursday, March 26, [http://mathsci.kaist.ac.kr/~jioon/ Ji Oon Lee], [http://www.kaist.edu/html/en/index.html KAIST] == | == Thursday, March 26, [http://mathsci.kaist.ac.kr/~jioon/ Ji Oon Lee], [http://www.kaist.edu/html/en/index.html KAIST] == |

## Revision as of 08:54, 3 February 2015

# Spring 2015

**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, January 15, Miklos Racz, UC-Berkeley Stats

Title: Testing for high-dimensional geometry in random graphs

Abstract: I will talk about a random geometric graph model, where connections between vertices depend on distances between latent d-dimensional labels; we are particularly interested in the high-dimensional case when d is large. Upon observing a graph, we want to tell if it was generated from this geometric model, or from an Erdos-Renyi random graph. We show that there exists a computationally efficient procedure to do this which is almost optimal (in an information-theoretic sense). The key insight is based on a new statistic which we call "signed triangles". To prove optimality we use a bound on the total variation distance between Wishart matrices and the Gaussian Orthogonal Ensemble. This is joint work with Sebastien Bubeck, Jian Ding, and Ronen Eldan.

## Thursday, January 22, No Seminar

## Thursday, January 29, Arnab Sen, University of Minnesota

Title: **Double Roots of Random Littlewood Polynomials**

Abstract: We consider random polynomials whose coefficients are independent and uniform on {-1,1}. We will show that the probability that such a polynomial of degree n has a double root is o(n^{-2}) when n+1 is not divisible by 4 and is of the order n^{-2} otherwise. We will also discuss extensions to random polynomials with more general coefficient distributions.

This is joint work with Ron Peled and Ofer Zeitouni.

## Thursday, February 5, No seminar this week

## Wednesday, February 11, Sam Stechmann, UW-Madison

Please note the unusual time and room.

Title: TBA

Abstract:

## Thursday, February 19, Xiaoqin Guo, Purdue

Title: TBA

Abstract:

## Thursday, February 26, Dan Crisan, Imperial College London

Title: TBA

Abstract:

## Thursday, March 5, Kurt Helms, Humboldt-Universität zu Berlin

Title: TBA

Abstract:

## Thursday, March 12, TBA

Title: TBA

Abstract:

## Thursday, March 19, Mark Huber, Claremont McKenna Math

Title: Understanding relative error in Monte Carlo simulations

Abstract: The problem of estimating the probability $p$ of heads on an unfair coin has been around for centuries, and has inspired numerous advances in probability such as the Strong Law of Large Numbers and the Central Limit Theorem. In this talk, I'll consider a new twist: given an estimate $\hat p$, suppose we want to understand the behavior of the relative error $(\hat p - p)/p$. In classic estimators, the values that the relative error can take on depends on the value of $p$. I will present a new estimate with the remarkable property that the distribution of the relative error does not depend in any way on the value of $p$. Moreover, this new estimate is very fast: it takes a number of coin flips that is very close to the theoretical minimum. Time permitting, I will also discuss new ways to use concentration results for estimating the mean of random variables where normal approximations do not apply.

## Thursday, March 26, Ji Oon Lee, KAIST

Title: TBA

Abstract:

## Thursday, April 2, No Seminar, Spring Break

## Thursday, April 9, Elnur Emrah, UW-Madison

Title: TBA

Abstract:

## Thursday, April 16, TBA

Title: TBA

Abstract:

## Thursday, April 23, Hoi Nguyen, Ohio State University

Title: TBA

Abstract:

## Thursday, April 30, TBA

Title: TBA

Abstract:

## Thursday, May 7, TBA

Title: TBA

Abstract: