# Difference between revisions of "Probability Seminar"

From UW-Math Wiki

(→Thursday, September 17,) |
(→Thursday, December 10, TBA) |
||

Line 37: | Line 37: | ||

== Thursday, November 26, No Seminar, Thanksgiving Break == | == Thursday, November 26, No Seminar, Thanksgiving Break == | ||

== Thursday, December 3, TBA == | == Thursday, December 3, TBA == | ||

− | == Thursday, December 10, | + | == Thursday, December 10, [http://www.case.edu/artsci/math/esmeckes/ Elizabeth Meckes], [http://www.case.edu/artsci/math/ Case Western Reserve University] == |

<!-- | <!-- | ||

Line 52: | Line 52: | ||

In recent years, tropical rainfall statistics have been shown to conform to paradigms of critical phenomena and statistical physics. In this talk, stochastic models will be presented as prototypes for understanding the atmospheric dynamics that leads to these statistics and extreme events. Key nonlinear ingredients in the models include either stochastic jump processes or thresholds (Heaviside functions). First, both exact solutions and simple numerics are used to verify that a suite of observed rainfall statistics is reproduced by the models, including power-law distributions and long-range correlations. Second, we prove that a stochastic trigger, which is a time-evolving indicator of whether it is raining or not, will converge to a deterministic threshold in an appropriate limit. Finally, we discuss the connections among these rainfall models, stochastic PDEs, and traditional models for critical phenomena. | In recent years, tropical rainfall statistics have been shown to conform to paradigms of critical phenomena and statistical physics. In this talk, stochastic models will be presented as prototypes for understanding the atmospheric dynamics that leads to these statistics and extreme events. Key nonlinear ingredients in the models include either stochastic jump processes or thresholds (Heaviside functions). First, both exact solutions and simple numerics are used to verify that a suite of observed rainfall statistics is reproduced by the models, including power-law distributions and long-range correlations. Second, we prove that a stochastic trigger, which is a time-evolving indicator of whether it is raining or not, will converge to a deterministic threshold in an appropriate limit. Finally, we discuss the connections among these rainfall models, stochastic PDEs, and traditional models for critical phenomena. | ||

---> | ---> | ||

− | |||

== == | == == |

## Revision as of 13:52, 10 August 2015

# Fall 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, September 17, Nicholas A. Cook, UCLA

Title: TBA

## Thursday, September 24, Reed Ogrosky, UW-Madison

TBA

## Thursday, October 8, No Seminar due to the Midwest Probability Colloquium

Midwest Probability Colloquium

## Thursday, October 15, Louis Fan, UW-Madison

## Thursday, October 22, Tom Kurtz, UW-Madison

## Thursday, October 29, Ecaterina Sava-Huss, Cornell

TBA