Feb 24, 2026 · I am analyzing a discrete-time Markov chain that can grow exponentially but also suffers from frequent, severe drops. I want to find the exact probability that it reaches a certain threshold …

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 17, 2022 · Then $\text {it's a Markov Chain}$ . If you use another definition : From the first line of each random walk and Markov Chain , I think a Markov chain models a type of random walk , but it …

Apr 13, 2026 · When I started doing machine learning in the 1990s, many practitioners and researchers used Hidden Markov Models which are measure theoretically isomorphic to Markov models, but have …

Jul 22, 2025 · For irreducible Markov chains, if a state is recurrent, then every other state in the state space is automatically recurrent as well. This holds analogously for positive recurrence and null …

Aug 14, 2016 · My question is a bit more basic, can the difference between the strong Markov property and the ordinary Markov property be intuited by saying: "the Markov property implies that a Markov …

Now in markov chain a steady state vector ( when effect multiplying or any kind of linear transformation on prob state matrix yield same vector) : qp=q where p is prob state transition matrix this means Y = …

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

Mar 11, 2015 · Markov's Inequality and its corollary Chebyshev's Inequality are extremely important in a wide variety of theoretical proofs, especially limit theorems. A previous answer provides an example.

Aug 1, 2023 · So apparently it is a different way of generalising the weak Markov property. Broadly speaking, I would like to know whether this property ($\star$) has a name and under what conditions …