Text only print
Full screen print
Tokyo Probability Seminar
Seminar information archive ～11/08｜Next seminar｜Future seminars 11/09～
| Date, time & place | Monday 16:00 - 17:30 122Room #122 (Graduate School of Math. Sci. Bldg.) |
|---|---|
| Organizer(s) | Makiko Sasada, Shuta Nakajima (Keio Univ.), Masato Hoshino (Science Tokyo), Masahisa Ebina (Science Tokyo) |
2026/11/16
16:00-17:30 Room #122 (Graduate School of Math. Sci. Bldg.)
The classroom is 002. We are having teatime from 15:20 in the common room on the second floor. Please join us.
Federica Iacovissi (Instituto Superior Técnico)
The Matrix Product Ansatz from a probabilistic viewpoint
The classroom is 002. We are having teatime from 15:20 in the common room on the second floor. Please join us.
Federica Iacovissi (Instituto Superior Técnico)
The Matrix Product Ansatz from a probabilistic viewpoint
[ Abstract ]
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.
