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- W2963358411 abstract "Nonlinear chains are probabilistic models commonly used in physics, biology, and the social sciences. In Markov influence systems (MIS), the transition probabilities of the chains change as a function of the current state distribution. This work introduces a renormalization framework for analyzing the dynamics of MIS. It comes in two independent parts: first, we generalize the standard classification of chain states to the dynamic case by showing how to parse graph sequences. We then use this framework tocarry out the bifurcation analysis of a few important MIS families.In particular, we show that irreducible MIS are almost alwaysasymptotically periodic. We also give an example of hyper-torpid mixing, where a stationary distribution is reached in super-exponential time, a timescale that cannot be achieved by any chain." @default.
- W2963358411 created "2019-07-30" @default.
- W2963358411 creator A5044565966 @default.
- W2963358411 date "2018-01-01" @default.
- W2963358411 modified "2023-09-26" @default.
- W2963358411 title "Toward a Theory of Markov Influence Systems and their Renormalization" @default.
- W2963358411 doi "https://doi.org/10.4230/lipics.itcs.2018.58" @default.
- W2963358411 hasPublicationYear "2018" @default.
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