Discrete Markov Method
a) Draw state space diagrams.
b) Find MTTF. If The system starts from the 1st state (20 hours).
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Statoinary Markov processes
1. Situation diagram the work of the continuous Markov system in Fig. There is a state change rate in units per hour. Find a) the finite probability of each state.b) System Availability c) the MTTF value of the system when a. State 1 is Normal, State 2 is Alternate and State 3 is Failure (100 hours). b. Status 1 is working state, state 2 and state 3 are fail state (50 hours).
P is the (one-step) transition probability matrix of a Markov chain with state space {0, 1, 2, 3,...
P is the (one-step) transition probability matrix of a Markov chain with state space {0, 1, 2, 3, 4 0.5 0.0 0.5 0.0 0.0 0.25 0.5 0.25 0.0 0.0 P=10.5 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.5 0.5 0.0 0.0 0.0 0.5 0.5/ (a) Draw a transition diagram. (b) Suppose the chain starts at time 0 in state 2. That is, Xo 2. Find E Xi (c)Suppose the chain starts at time 0 in any of the states with...
ematics of Discrete-Time Markov Chaill Develop a Markov chain model for each of the following situations....
ematics of Discrete-Time Markov Chaill Develop a Markov chain model for each of the following situations. Assume that the process is oh after each play and that Pw 0.4. Find the transient probabilities for 10 plays as well as the state and absorbing state probabilities when appropriate. (a) For steady- the given situation, let the states be the cash supply: S0, 10, 20, 30, and 40. In addition , find the first passage probabilities from the initial state to the...
Express the following transfer functions, H(s) in a state variable a) 1st Companion Form b) Jordan...
Express the following transfer functions, H(s) in a state variable a) 1st Companion Form b) Jordan Form. Draw Block Diagrams showing outputs. Check controllability and observability of the state space equations. H(s) = 1/(s2+4s+4)
Express the following transfer functions, H(s) in a state variable a) 1st Companion Form b) Jordan...
Express the following transfer functions, H(s) in a state variable a) 1st Companion Form b) Jordan Form. Draw Block Diagrams showing outputs. Check controllability and observability of the state space equations. H(s) = (8x+7)/(s3+4s2+6s+8)
Consider a Markov chain with state space S = {1,2,3,4} and transition matrix P = where...
Consider a Markov chain with state space S = {1,2,3,4} and transition matrix P = where (a) Draw a directed graph that represents the transition matrix for this Markov chain. (b) Compute the following probabilities: P(starting from state 1, the process reaches state 3 in exactly three-time steps); P(starting from state 1, the process reaches state 3 in exactly four-time steps); P(starting from state 1, the process reaches states higher than state 1 in exactly two-time steps). (c) If the...
Consider a Markov chain with state space S = {1, 2, 3, 4} and transition matrix...
Consider a Markov chain with state space S = {1, 2, 3, 4} and transition matrix P= where (a) Draw a directed graph that represents the transition matrix for this Markov chain. (b) Compute the following probabilities: P(starting from state 1, the process reaches state 3 in exactly three time steps); P(starting from state 1, the process reaches state 3 in exactly four time steps); P(starting from state 1, the process reaches states higher than state 1 in exactly two...
1. Exit times. Let X be a discrete-time Markov chain (with discrete state space) and suppose...
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...
Suppose that we have a finite irreducible Markov chain Xn with stationary distribution π on a...
Suppose that we have a finite
irreducible Markov chain Xn with stationary distribution π on a
state space S. (a) Consider the sequence of neighboring pairs, (X0,
X1), (X1, X2), (X2, X3), . . . . Show that this is also a Markov
chain and find the transition probabilities. (The state space will
be S ×S = {(i,j) : i,j ∈ S} and the jumps are now of the form (i,
j) → (k, l).) (b) Find the stationary distribution...
1. (15 points) For each of the following Markov Chains: specify the classes, determine whether they...
1. (15 points) For each of the following Markov Chains: specify the classes, determine whether they are transient or recurrent, draw state transition diagrams, find if any absorbent states, and write whether or not each of the chains is irreducible. (a) (5 points) 0.5 0.5 0 0 (b) (5 points) 2 0o P2=1 0 0 1 0 0 (c) (5 points) P3 = 4 2 4
P is the (one-step) transition probability matrix of a Markov chain with state space {0, 1, 2, 3, 4 0.5 0.0 0.5 0.0 0.0 0.25 0.5 0.25 0.0 0.0 P=10.5 0.0 0.5 0.0 0.0 0.0 0.0 0.0 0.5 0.5 0.0 0.0 0.0 0.5 0.5/ (a) Draw a transition diagram. (b) Suppose the chain starts at time 0 in state 2. That is, Xo 2. Find E Xi (c)Suppose the chain starts at time 0 in any of the states with...
ematics of Discrete-Time Markov Chaill Develop a Markov chain model for each of the following situations. Assume that the process is oh after each play and that Pw 0.4. Find the transient probabilities for 10 plays as well as the state and absorbing state probabilities when appropriate. (a) For steady- the given situation, let the states be the cash supply: S0, 10, 20, 30, and 40. In addition , find the first passage probabilities from the initial state to the...
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...
Suppose that we have a finite
irreducible Markov chain Xn with stationary distribution π on a
state space S. (a) Consider the sequence of neighboring pairs, (X0,
X1), (X1, X2), (X2, X3), . . . . Show that this is also a Markov
chain and find the transition probabilities. (The state space will
be S ×S = {(i,j) : i,j ∈ S} and the jumps are now of the form (i,
j) → (k, l).) (b) Find the stationary distribution...
1. (15 points) For each of the following Markov Chains: specify the classes, determine whether they are transient or recurrent, draw state transition diagrams, find if any absorbent states, and write whether or not each of the chains is irreducible. (a) (5 points) 0.5 0.5 0 0 (b) (5 points) 2 0o P2=1 0 0 1 0 0 (c) (5 points) P3 = 4 2 4
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