ArticleThe Journal of chemical physics2012
A derivation of the master equation from path entropy maximization.
Article in The Journal of chemical physics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Inferring effective neuronal circuits via network flux counting.PLoS computational biology · 2026Article
- A principled basis for nonequilibrium network flows.Nature communications · 2026Article
- Avoiding matrix exponentials for large transition rate matrices.The Journal of chemical physics · 2024Article
- Article
- Inferring a network from dynamical signals at its nodes.PLoS computational biology · 2020Article
- The Maximum Caliber Variational Principle for Nonequilibria.Annual review of physical chemistry · 2020Review
- Quantitative Kinetic Models from Intravital Microscopy: A Case Study Using Hepatic Transport.The journal of physical chemistry. B · 2019Article
- An introduction to the maximum entropy approach and its application to inference problems in biology.Heliyon · 2018Review
- Bistability, non-ergodicity, and inhibition in pairwise maximum-entropy models.PLoS computational biology · 2017Article
- Single molecule conformational memory extraction: p5ab RNA hairpin.The journal of physical chemistry. B · 2014Article
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Authors and funding
2 authors.
Funding
Abstract
The master equation and, more generally, Markov processes are routinely used as models for stochastic processes. They are often justified on the basis of randomization and coarse-graining assumptions. Here instead, we derive nth-order Markov processes and the master equation as unique solutions to an inverse problem. We find that when constraints are not enough to uniquely determine the stochastic model, an nth-order Markov process emerges as the unique maximum entropy solution to this otherwise underdetermined problem. This gives a rigorous alternative for justifying such models while providing a systematic recipe for generalizing widely accepted stochastic models usually assumed to follow from the first principles.
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Registered trials
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