Dec 8, 2022 · To sum up, Markov kernels are a formal way to set up conditional distributions. (2) is precisely the part of the definition that captures this aspect, while (1) is needed for technical reasons …

Apr 25, 2017 · A Markov process is basically a stochastic process in which the past history of the process is irrelevant if you know the current system state. In other words, all information about the …

I would like to know what books people currently like in Markov Chains (with syllabus comprising discrete MC, stationary distributions, etc.), that contain many good exercises. Some such book on

Feb 27, 2024 · The time it spends in each state converge to $\frac12$, but the probability to be in a state is entirely determined by whether the time is even or odd. A Markov chain on a finite space is called …

Jun 6, 2025 · In a Markov process, a null recurrent state is returned to, but just not often enough for the return to be classified as periodic with any finite period. (eg. returning, on average once every 4.5 …

Jul 23, 2010 · 7 Markov chains, especially hidden Markov models are hugely important in computation linguistics. A hidden Markov model is one where we can't directly view the state, but we do have …

Jun 17, 2022 · I think Surb means any Markov Chain is a random walk with Markov property and an initial distribution. By "converse" he probably means given any random walk , you cannot conclude …

Jul 27, 2023 · It is easy to show that the Markov property implies that "past and future are independent given the present". Is if the reverse implication also true (as John Dawkins's answer to this question …

Apr 14, 2024 · Theorem 1 (The Fundamental Theorem of Markov Chains): Let X0,X1, … X 0, X 1, be a Markov chain over a finite state space, with transition matrix P P. Suppose that the chain is …

Mar 29, 2024 · @AugustoSantos although it may not be Markov, based on the entries in the above matrix, can one determine probabilities of starting in states 1 or 2 and transitioning to 3 and vice versa?