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Bookmark or Share- Markov process vs. markov chain vs. random process vs. stochastic ...When the Markov process is discrete-valued (i.e discrete state space), it is called a Markov chain. Markov chain can further be divided into Continuous –parameter (or time) Markov chain and discrete – parameter (or time) Markov chain for continuous and discrete parameters respectively. Random process and stochastic process are synonymous.
- 'Intuitive' difference between Markov Property and Strong Markov PropertyWikipedia states: "The strong Markov property implies the ordinary Markov property, since by taking the stopping time T=t, the ordinary Markov property can be deduced." Is that correct? I think the problem with this is my background, at my university we take stochastic processes first, followed by measure theory.
- Why Markov matrices always have 1 as an eigenvalueNow 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 = 1 as an eigen vector of P' which also means
- Proof of Gauss-Markov theorem - Mathematics Stack ExchangeThe Gauss-Markov theorem states that, under the usual assumptions, the OLS estimator $\beta_{OLS}$ is BLUE (Best Linear Unbiased Estimator). To prove this, take an arbitrary linear, unbiased estimator $\bar{\beta}$ of $\beta$.
- What does the steady state represent to a Markov Chain?Some Markov chains do not have stable probabilities. For example, if the transition probabilities are given by the matrix $$ \begin{bmatrix}0 & 1\\1 & 0\end{bmatrix}, $$ and if the system is started off in State 1, then the probability of finding the system in State 1 will oscillate between 0 and 1 forever.
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