Feb 8, 2026 · 0 On page 497 of Introduction to Probability by Blitzstein and Hwang, they define the Markov chain as follows:

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 …

Sep 30, 2023 · A Gauss-Markov process is a random process that is both a Gaussian process and a Markov process. What is the difference between them? Are there Gauss-Markov processes that are …

Sep 27, 2024 · This mapping between deterministic systems and Markov chain is a useful bridge. For example in the study of dynamical systems, this allows you to reduce the study of chaotic maps to …

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 …

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 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 $X_0, X_1, \ldots$ be a Markov chain over a finite state space, with transition matrix $P$. Suppose that the chain is …

May 14, 2020 · Action of a toral automorphism on a Markov partition Ask Question Asked 5 years, 9 months ago Modified 5 years, 9 months ago

4 days ago · Stochastic processes (with either discrete or continuous time dependence) on a discrete (finite or countably infinite) state space in which the distribution of the next state depends only on the …