Markov processes form a fundamental class of stochastic models in which the evolution of a system is delineated by the memoryless property. In such processes, the future state depends solely on the ...

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

A Markov-modulated Poisson Process (MMPP) is a Poisson process that has its parameter controlled by a Markov process. These arrival processes are typical in communications modeling where time-varying ...

If we can ‘talk’ to AI programs today, it’s in part because of a Russian from the 1800s. Markov’s approach to data in flux changed how we navigate our world. There’s an odd little trick to how AI ...