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Room P3.10, Mathematics Building
Federica Iacovissi, Instituto Superior Técnico, Universidade de Lisboa.
From the Matrix Product Ansatz to Hidden Markov Structures.
Nonequilibrium stationary measures are generally not known explicitly and depend on the specific microscopic dynamics of the system. For some interacting particle systems, however, they admit an exact representation in terms of the Matrix Product Ansatz (MPA), an algebraic construction based on ordered products of matrices.
In this talk, we provide a probabilistic characterization of the class of probability measures that can be represented by the MPA. We introduce a constructive procedure, based on a suitable enlargement of the state space, showing that a probability measure admits a representation in terms of non-negative matrices via the MPA if and only if it can be expressed as a mixture of inhomogeneous product measures, where the mixing law is given by a Markov bridge.
We illustrate this construction through several 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.