Markov processes
A Markov process is a temporal stochastic process with the property that the future is conditionally independent of the past, given the present.
Literature
Cosma Shalizi’s notebooks on Markov models , hidden Markov models , and inference for such offer a much more comprehensive annotated bibliography.
Category theory
As part of their network theory program, Baez and collaborators have written several papers about open Markov processes:
- Baez, Fong, Pollard, 2016: A compositional framework for Markov processes (doi, arxiv, nCat Cafe , Azimuth )
- Baez & Pollard, 2017: A compositional framework for reaction networks (doi,
arxiv, nCat Cafe , Azimuth )
- Pollard, 2017, PhD thesis: Open Markov processes and reaction networks (arxiv)
- Baez & Courser, 2018: Coarse-graining open Markov processes (pdf, arxiv, nCat Cafe , Azimuth )