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 …

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 = …

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 …

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 (⋆ ⋆) has a name and under what conditions it is …

Jul 18, 2015 · I try to find as much as possible cases, when the chain Z(t) =|X1(t) −X2(t)| Z (t) = | X 1 (t) X 2 (t) | is Markov, where X1(t) X 1 (t) and X2(t) X 2 (t) are independent, discrete-time and space, …

Sep 20, 2023 · Markov Process with time varying transition probabilities. Ask Question Asked 2 years, 4 months ago Modified 2 years, 4 months ago

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 …

A more general problem seems to be "Given any Markov chain (i.e., not a degenerate one), group the states into sets: what are the conditions under the resulting process satisfies the Markov assumption?"

Mar 15, 2021 · I'm currently studying a module on Stochastic Processes and I'm quite confused as to the interpretation of Global/Local Balanced Equations for Discrete Markov Chains. From what I've learnt, …