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 …

Feb 11, 2026 · A complication is that we're working in the time-inhomogeneous case; I found Norris' Markov Chains to be a good reference for the time-homogeneous case and worked out Chapters 1 …

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?

Feb 8, 2023 · You cannot "prove" Markov property, unless you are given some property of your chain beforehand (Markov property is often a part of the definition of a Markov chain)

Nov 27, 2024 · Yes, this is a version of the Chapman-Kolmogorov equation. Let $ (Z_0, Z_1, Z_2)$ be a Markov chain taking values in some finite or countable space.

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

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 …

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 …

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 …

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 current …