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

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 …

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 …

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 …

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

Nov 12, 2024 · As a learning exercise, I am trying to learn how to derive the expected time needed in a (discrete time) Markov Chain to first reach a certain state. For example, suppose we have a 5 state …

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 …

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 …

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 …