本文印刷
全画面プリント
東京確率論セミナー
過去の記録 ～11/08｜次回の予定｜今後の予定 11/09～
| 開催情報 | 月曜日 16:00～17:30 数理科学研究科棟(駒場) 122号室 |
|---|---|
| 担当者 | 佐々田槙子、中島秀太（慶應義塾大学）、星野壮登（東京科学大学）、蛯名真久（東京科学大学） |
| セミナーURL | https://sites.google.com/view/tokyo-probability-seminar23/ |
2026年11月16日(月)
16:00-17:30 数理科学研究科棟(駒場) 122号室
教室は002です。15:20〜 ２階のコモンルームでTea timeを行います。ぜひこちらにもご参加ください。
Federica Iacovissi 氏 (Instituto Superior Técnico)
The Matrix Product Ansatz from a probabilistic viewpoint
教室は002です。15:20〜 ２階のコモンルームでTea timeを行います。ぜひこちらにもご参加ください。
Federica Iacovissi 氏 (Instituto Superior Técnico)
The Matrix Product Ansatz from a probabilistic viewpoint
[ 講演概要 ]
We provide a probabilistic characterization of the class of probability measures that can be represented by the Matrix Product Ansatz (MPA). We describe a constructive procedure, based on a suitable enlargement of the state space, showing that a probability measure can be expressed in terms of non-negative matrices via the MPA if and only if it can be written as a mixture of inhomogeneous product measures, where the mixing law is given by a Markov bridge. We illustrate this construction by applying it to examples of interacting particle systems. Finally, we discuss how the resulting probabilistic structure can be exploited to obtain large deviation principles for this class of measures. Joint work with Davide Gabrielli.
We provide a probabilistic characterization of the class of probability measures that can be represented by the Matrix Product Ansatz (MPA). We describe a constructive procedure, based on a suitable enlargement of the state space, showing that a probability measure can be expressed in terms of non-negative matrices via the MPA if and only if it can be written as a mixture of inhomogeneous product measures, where the mixing law is given by a Markov bridge. We illustrate this construction by applying it to examples of interacting particle systems. Finally, we discuss how the resulting probabilistic structure can be exploited to obtain large deviation principles for this class of measures. Joint work with Davide Gabrielli.
