Markov Chains
Discrete- and continuous-time chains, stationarity, mixing, and coupling.
Markov Chains. Discrete- and continuous-time chains, stationarity, mixing, and coupling. This page collects canonical references that organise the subject and provide entry points to its main techniques.
Foundations and canonical references
The standard treatments of markov chains approach the subject from complementary angles. Meyn, Markov Chains and Stochastic Stability (2009) is the anchor reference for the subject and lays out the core definitions, theorems, and worked examples that practitioners return to. Norris, Markov Chains (1997) gives a parallel, more proof-oriented exposition of the same material and is widely used as a graduate text.
Open methodological questions for markov chains include sharpening the bridges between foundational theory and computational practice, extending classical results to broader or more structured settings, and integrating the techniques surveyed above with adjacent mathematical disciplines. The references listed in this page are the entry points that current work builds on.
Prerequisites
Sources
- textbook · primary · 2009Markov Chains and Stochastic Stabilitymeyn-2009, tweedie-2009
- textbook · primary · 1997Markov Chainsnorris-1997
In context
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