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- probability - How to prove the tightness of Markovs bound . . .
Using Markov's bound, we can show at most 1 k 1 k But how to show equality?! My question to be exact what is the idea to prove the tightness of this bound!
- Book on Markov Decision Processes with many worked examples
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 some problems during long commutes
- Predict Weather Using Markov Chains - Mathematics Stack Exchange
Explore related questions probability markov-chains See similar questions with these tags
- Relationship between Eigenvalues and Markov Chains
The solutions to this equation are the eigenvalues of A Markov Chains For an m state Discrete Time Markov Chain, we can write its Transition Matrix as (the probability of transitioning from any state i to state j):
- When does equality in Markovs inequality occur? [duplicate]
You can also get equality when X only takes the values 0 or a, each with non-zero probability Suppose X takes the values 0 or a with probability 1 − p and p respectively Then E(X) = ap and P(X ⩾ a) = p, so we have equality in Markov's inequality
- Understanding the first step analysis of absorbing Markov chains
which is the key point of the so called "first step analysis" See for instance Chapter 3 in Karlin and Pinsky's Introduction to Stochastic Modeling But the book does not bother giving a proof of it Here is my question: How can one prove (*) using the definition of conditional probability and the Markov property?
- probability - How to prove that a Markov chain is transient . . .
I can see, by intuition, that for all values of p where p ≠ 1 2, the Markov chain is transient This is because when p> 1 2, there is a higher probability of moving to the right than to the left, for every state i
- Amount of time spent in each state by a Markov chain with matrix of . . .
Y is not a Markov chain, because at time n, the (n + 1)th transition time depends on the (n + 1)th state So we cannot take the approach of finding a stationary distribution for a transition matrix
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