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統計数学セミナー
過去の記録 ～04/30｜次回の予定｜今後の予定 05/01～
| 担当者 | 吉田朋広、増田弘毅、荻原哲平、小池祐太 |
|---|---|
| 目的 | 確率統計学およびその関連領域に関する研究発表, 研究紹介を行う. |
2021年11月17日(水)
15:30-17:00 数理科学研究科棟(駒場) 号室
Zoomでの開催となります。参加希望の方はGoogle Formより２日前までにご登録ください。
Jean Bertoin 氏 (Institut of Mathematics, University of Zurich (UZH))
On the local times of noise reinforced Bessel processes
https://docs.google.com/forms/d/e/1FAIpQLSeuK9AOw6QUqvUge9ukw__v04j5jpfogzrGxlPLpEgNhW09kg/viewform
Zoomでの開催となります。参加希望の方はGoogle Formより２日前までにご登録ください。
Jean Bertoin 氏 (Institut of Mathematics, University of Zurich (UZH))
On the local times of noise reinforced Bessel processes
[ 講演概要 ]
Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Bessel processes form a one-parameter family of self-similar diffusion on $[0,\infty)$ with the same Hurst exponent 1/2 as Brownian motion. Loosely speaking, in this setting, linear noise reinforcement with reinforcement parameter $p$ consists of repeating (if $p>0$) or counterbalancing (if $p<0$)infinitesimal increments of the process, uniformly at random and at a fixed rate as time passes. In this talk, we will investigate the effect of noise reinforcement on the local time at level $0$, that is, informally, the time that the process spends at $0$. A connection with increasing self-similar Markov processes will play a key role.
[ 参考URL ]Asia-Pacific Seminar in Probability and Statistics (APSPS)
https://sites.google.com/view/apsps/home
Bessel processes form a one-parameter family of self-similar diffusion on $[0,\infty)$ with the same Hurst exponent 1/2 as Brownian motion. Loosely speaking, in this setting, linear noise reinforcement with reinforcement parameter $p$ consists of repeating (if $p>0$) or counterbalancing (if $p<0$)infinitesimal increments of the process, uniformly at random and at a fixed rate as time passes. In this talk, we will investigate the effect of noise reinforcement on the local time at level $0$, that is, informally, the time that the process spends at $0$. A connection with increasing self-similar Markov processes will play a key role.
https://docs.google.com/forms/d/e/1FAIpQLSeuK9AOw6QUqvUge9ukw__v04j5jpfogzrGxlPLpEgNhW09kg/viewform
