We covered Markov chains in class and after going through the details, I still have a few questions. (I encourage you to give short answers to the question, as this may become very …

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 …

Dec 25, 2020 · All states of a finite irreducible Markov chain are recurrent. As irreducible Markov chains have one class, statement 1 1 implies all states are either transient or recurrent.

Oct 5, 2023 · probability probability-theory solution-verification markov-chains random-walk See similar questions with these tags.

I am looking for a book (or online article (s)) on Markov decision processes that contains lots of worked examples or problems with solutions. The purpose of the book is to grind my teeth on …

6 Tim's characterization of states in terms of closed sets is correct for finite state space Markov chains. Partition the state space into communicating classes. Every recurrent class is closed, …

Note that this version of the Riesz-Markov-Kakutani theorem is much stronger than the usually stated one, which is concerned positive functionals on $\mathbb {R}$. The fact that the dual …

May 18, 2015 · Transition probabilities of Markov Chains most definitely can depend on time. The ones that don't are called time-homogeneous. For instance in a discrete time discrete state …

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 …

5 Discrete-time continuous state Markov processes are widely used. Autoregressive processes are a very important example. Actually, if you relax the Markov property and look at discrete …