Large Deviations for Degenerate Markov Jump Processes

Andrea Agazzi (Duke University)

09-Dec-2021, 16:00-16:30 (2 years ago)

Abstract: The dynamics of a network of chemical reactions under the laws of mass action kinetics are typically modeled as a system of coupled ordinary differential equations. This macroscopic model can be recovered, under the appropriate scaling, as the functional law of large numbers for a family of jump Markov processes capturing the discrete nature of the underlying, microscopic dynamical model. The large deviations behavior of these models has been recently investigated under relatively strong assumptions on the existence of reactions with rates bounded away from 0, allowing to guarantee the nondegeneracy of the Markov process being investigated. We show that these assumptions, which are violated by many models of interest, can be significantly relaxed, establishing large deviations principles for a large class of degenerate jump Markov processes.

algebraic geometrydynamical systemsprobability

Audience: researchers in the topic

( video )


Seminar on the Mathematics of Reaction Networks

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This seminar series focuses on progress in mathematical theory for the study of reaction networks, mainly in biology and chemistry. The scope is broad and accommodates works arising from dynamical systems, stochastics, algebra, topology and beyond.

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The organizers.

Organizers: Daniele Cappelletti*, Stefan Müller*, Tung Nguyen*, Polly Yu*
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