Kushal Kr. Dey, Sourabh Bhattacharya, A brief tutorial on transformation based Markov Chain Monte Carlo and optimal scaling of the additive transformation, Brazilian Journal of Probability and ...

Traditionally Markov chains are software state machines that transition between states with given probabilities, often learned from a training corpus.

APPM 4560/5560 Markov Processes, Queues, and Monte Carlo Simulations Brief review of conditional probability and expectation followed by a study of Markov chains, both discrete and continuous time.

High-order Markov chain models extend the conventional framework by incorporating dependencies that span several previous states rather than solely the immediate past.