Problem 2: (mean return time) Consider a Markov chain {X,} on states {0,1,2,3} with a tran- sition matrix 1 0.1 0.4 0.2 0.3 P = 0.2 0.2 0.5 0.1 0.3 0.3 0.4 1. Compute the limiting distribution (T0, T1, T2, T3) of this Markov Markov Chain%; 2. For each state i, compute (directly) m¡¡ - the average number of steps it takes to get back to i if started in i, and show that the relation m;; = 1/T; is true.
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- Question 3. Recall that the discrete stochastic process {Xn, n = 0, 1, 2,...} is a Markov chain if for each n, P(Xn+1 = j|Xn = i, Xn-1 = in-1,..., Xo = io) = P(Xn+1 = j|Xn = i) = Pij. Let {Xn, n = 0, 1, 2,...} be a Markov chain, does the following hold as well? P(Xn+2=jXn = i, Xn-1 = in-1, ..., Xo = io) = P(Xn+2 = j|Xn = i) Give a proof if you think it is true, otherwise give a counterexample. Hint: You may use the law of total probability for conditional probability without proof. Please step by step answer.Suppose that a production process changes state according to a Markov chain on [25] state space S = {0, 1, 2, 3} whose transition probability matrix is given by a) Determine the limiting distribution for the process. b) Suppose that states 0 and 1 are “in-control,” while states 2 and 3 are deemed “out-of-control.” In the long run, what fraction of time is the process out-of-control?Let {Xn, n ≥ 0} be a Markov chain with three states 0, 1, 2 and has the transition probability matrix, P= [ 3/4 1/4 0 1/4 1/2 1/4 0 3/4 1/4 ] The initial distribution is, P {Xn; n = 0, 1, 2} = 1/3 a. What is P {X1 = 1| X0 = 2}?b. What is P {X2 = 2| X1 = 1}?c. What is P {X2 = 2, X1 = 1| X0 = 2}?d. What is P {X2 = 2, X1 = 1, X0 = 2}?e. What is P {X3 = 1, X2 = 2, X1 = 1, X0 = 2}?
- P is the transition matrix for a Markov chain with two states. X0 is the initial state vector for the population. Find x1 & x2, and find the steady state vector.For the attached transition probability matrix for a Markov chain. a) Compute P{X2 = 3|X0 = 3}.b) Compute limn→∞ P{Xn = 2|X0 = 4}.(Let x denote this probability, and condition on the first transition when the Markov chain begins in state 4.)1. Define a Stochastic process and briefly discuss the meaning of measurability of a stochastic process. 2. Consider the ARMA(1,1) model yt = 0.8yt-1 + et + 0.5et-1 with et ~ WN(0, σe2). Derive the Wold representation of yt. 3. Consider the ARMA(2,1) process Φ(L)Xt = Θ(L)et with Φ(L) = 1 − 1.3L + 0.4L2 , Θ(L) = 1 + 0.4L and et ∼ WN(0, σe2). Obtain its Wold representation. 4.Consider the ARMA(2,2) process given by Xt =0.4Xt−1+0.45Xt−2+et+et−1+0.25et−2 with et ∼WN(0,σe2). 5. Consider the MA(1) process yt = et+1.5et−1 with et ∼ WN(0,σe2). Is the above MA(1) a Wold representation? Why or Why not? If not, obtain a suitable Wold representation.
- Consider a Markov process with state space S= {1,2,3} and transition matrix P. p= p q 0 1/2 0 1/2 p-1/2 7/10 1/5 Find the values of p and qFor the attached transition probability matrix for a Markov chain with {Xn ; n = 0, 1, 2,.........}: a) How many classes exist, and which two states are the absorption states? b) What is the limn->inf P{Xn = 3 | X0 = 3}? b) What is the limn->inf P{Xn = 1 | X0 = 3}?The figure above illustrates a continuous-time Markov process. Suppose the system is currently instate 2. After a small amount of time Δ, what is the probability that the system is in each state?
- A Markov chain {Xn} on the states 0, 1, 2 has the transition probability matrix, P = [ 0.1 0.2 0.7 0.2 0.2 0.6 0.6 0.1 0.3 ] a. Compute the two-step transition matrix, P2. b. What is P {X3 = 1| X1 = 0}?c. What is P {X3 = 1| X0 = 0}?If Kt = B2t - t, where B is standard Brownian Motion, show that Kt is a martingale, and a markov process1- The number of items produced in a factory during a week is known to be a randomvariable with mean 50● Using Markov's inequality, what can you say about the probability that this week'sproduction exceeds 75?● If the variance of one week's production is equal to 25, then using Chebyshev'sinequality, what can be said about the probability that this week's production isbetween 40 and 60?