7 process Let In, n= 0, L ... be a Marko v chain (a discrete Markou) with P(Xo = 0, X, - 1) = P(Xo = 0, x2 - 1) = P(...
Let Xo, X1,... be a Markov chain with transition matrix 1(0 1 0 P 2 0 0 1 for 0< p< 1. Let g be a function defined by g(x) =亻1, if x = 1, if x = 2.3. , Let Yn = g(x,), for n 0. Show that Yo, Xi, is not a Markov chain.
This is for Stochastic Processes Let Xo, Xi,... be a Markov chain whose state space is Z (the integers). Recall the Markov property: P(X, _ in l Xo-to, X1-21, , Xn l-an l)-P(Xn-in l x, i-İn 1), Vn, Vil. Does the following always hold: (lProve if "yes", provide a counterexample if "no") Let Xo, Xi,... be a Markov chain whose state space is Z (the integers). Recall the Markov property: P(X, _ in l Xo-to, X1-21, , Xn l-an l)-P(Xn-in...
Let (X.) be a Marko chain with the state space (1.2,3) and transition proba- bility matrix 0 4 6 P 25 75 0 4 0 6 Let the initial distribution be q(0) [1(0), q2(0), s(0) [0.4, 0.2, 0.4] (a) Find ELX. (b) Calculate PlX,-2, X,-2, X,-11X,-1]. (c) To what matrix will the n-step transition probability matrix converge when n is very large? Your solution should be accurate to two decimal places.
1. Exit times. Let X be a discrete-time Markov chain (with discrete state space) and suppose pii > 0. Let T =min{n 21: X i} be the exit time from state i. Show that T has a geometric distribution with respect to the conditional probability P 1. Exit times. Let X be a discrete-time Markov chain (with discrete state space) and suppose pii > 0. Let T =min{n 21: X i} be the exit time from state i. Show that...
1. Let Xn be a Markov chain with states S = {1, 2} and transition matrix ( 1/2 1/2 p= ( 1/3 2/3 (1) Compute P(X2 = 2|X0 = 1). (2) Compute P(T1 = n|Xo = 1) for n=1 and n > 2. (3) Compute P11 = P(T1 <0|Xo = 1). Is state 1 transient or recurrent? (4) Find the stationary distribution à for the Markov Chain Xn.
Please answer this in specific way,thanks. 1. A Markov chain X = (X2) >0 with state space I = {A, B, C} has a one-step transition matrix P given by 70 2/3 1/3) P= 1/3 0 2/3 (1/6 1/3 1/2) (a) Find the eigenvalues 11, 12, 13 of P. (b) Deduce pn can be written as pn = 10 + XU, + Aug (n > 0) and determine the matrices U1, U2, U3 by using the equations n = 0,1,2....
Q6 (4+3+3+ 6=16 marks) Let Xo, X1, X2 be three distinct real numbers. For polynomials p(x) and q(x), define < p(x),q(x) >= p(xo)q(x0) + p(x1)q(x1) + p(x2)q(22). Let p(n) denote the vector space of all polynomials with degree more no than n. (i) Show that < .. > is an inner product in P(2). (ii) Is < ... > an inner product in P(3)? Explain why. (iii) Is <,:> an inner product in P(1)? Explain why. (iv) Consider Xo =...
5. Let X n 2 0} be a Markov chain with state space S = {0,1,2,...}. Suppose P{Xn+1 = 0|X,p = 0 3/4, P{Xn+1 = 1\Xn, P{Xn+1 = i - 1|X, 0 1/4 and for i > 0, P{X+1 = i + 1|X2 = i} i} 3/4. Compute the long run probabilities for this Markov chain = 1/4 and =
Let X0,X1,... be a Markov chain whose state space is Z (the integers). Recall the Markov property: P(Xn = in | X0 = i0,X1 = i1,...,Xn−1 = in−1) = P(Xn = in | Xn−1 = in−1), ∀n, ∀it. Does the following always hold: P(Xn ≥0|X0 ≥0,X1 ≥0,...,Xn−1 ≥0)=P(Xn ≥0|Xn−1 ≥0) ? (Prove if “yes”, provide a counterexample if “no”) Let Xo,Xi, be a Markov chain whose state space is Z (the integers). Recall the Markov property: P(X,-'n l Xo-io, Xi...
4. Let Z1, Z2,... be a sequence of independent standard normal random variables. De- fine Xo 0 and n=0, 1 , 2, . . . . TL: n+1 , The stochastic process Xn,n 0, 1,2,3 is a Markov chain, but with a continuous state space. (a) Find EXn and Var(X). (b) Give probability distribution of Xn (c) Find limn oo P(X, > є) for any e> 0. (d) Simulate two realisations of the Markov process from n = 0 until...