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 …

Jun 17, 2022 · Then 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 …

Jan 1, 2023 · For two states $x,y$ in $E$, let $p^n (x,y)$ denote the $n$ -step Markov chain transition probability from $x$ to $y$. Then the period of a point $x$ is the greatest common …

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, …

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 …

Almost, but you need "greater than or equal to." We have: $$ H (X|Y) = H (X|Y,Z) \leq H (X|Z) $$ where the first equality is from the Markov structure and the final inequality is because …

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 …

Even stochastic processes arising from Newtonian physics don't have the Markov property, because parts of the state (say, microscopic degrees of freedom) tend not to be observed or …

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 …

Mar 26, 2021 · Is this a type of Markov operator? (The infinitesimal generator is also an operator on measurable functions). What's the equivalence between these two definitions and what's …