In Exercises 1–6, consider a Markov chain with state space with {1, 2, …, n} and the given transition matrix. Find the communication classes for each Markov chain, and state whether the Markov chain is reducible or irreducible.
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- Explain how you can determine the steady state matrix X of an absorbing Markov chain by inspection.arrow_forward12. Robots have been programmed to traverse the maze shown in Figure 3.28 and at each junction randomly choose which way to go. Figure 3.28 (a) Construct the transition matrix for the Markov chain that models this situation. (b) Suppose we start with 15 robots at each junction. Find the steady state distribution of robots. (Assume that it takes each robot the same amount of time to travel between two adjacent junctions.)arrow_forwardConsider the Markov chain whose matrix of transition probabilities P is given in Example 7b. Show that the steady state matrix X depends on the initial state matrix X0 by finding X for each X0. X0=[0.250.250.250.25] b X0=[0.250.250.400.10] Example 7 Finding Steady State Matrices of Absorbing Markov Chains Find the steady state matrix X of each absorbing Markov chain with matrix of transition probabilities P. b.P=[0.500.200.210.300.100.400.200.11]arrow_forward
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- 1. Identify all absorbing states in the Markov chains having the following matrix. Decide whether the Markov chain is absorbing. 1 2 3 1[ 1 a) 2 0.3 0.5 0.2 3 1. 1 4 [0.6 0 0.4 01 1 a) 2 0.9 0.1 0 0 0 4 2. Find the first three powers of each of the transition matrix. For each transition matrix, find the probability that state 1 changes to state 2 after three repetition of the experiment. a) C = l0.72 0.28 0.5 [0.8 0.1 0.1] b) E = |0.3 0.6 0.1 1arrow_forwardPlease describe the steps you used to get the solution to the problem provided in the image below.arrow_forwardLinear Algebraarrow_forward
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