Reference no: EM132204761

1. Steady State Probability of Markov Chains are

1. States of condition that reach probability value equal to 1.0 in all cases

2. Is the convergence to an equilibrium or “steady state” condition and applies to all markov chains

3. Steady State Probabilities is the product of Steady State Probabilities multiplied by the Transition Matrix

4. All of the above

5. None of the above

2. A Transition matrix is

1. Current states of a system at time t

2. Conditional probabilities that involve moving from one state to another

3. The stationary assumption of a markov chain

4. A m by n matrix of probabilities

5. none of the above

3. In a transition matrix where the sum probabilities values in each column equals 1.0 is referred to as

1. Steady State Probabilities

2. Conditional Probability Matrix

3. Stationary Matrix

4. Double Stochastic Transition Matrix

5. None of the above

4. Which of the following is true regarding the markov Analysis Methodology

1. States of Nature are outcomes of a process (machine operating or broken, % of customers buying product A & B, etc)

2. There exist an initial probability associated with the state of nature (100% operational and 0% broken, 80% customers buy product A and 20% buy product B)

3. There is also transition (or conditional) probabilities of moving from one state to another (represented by the Transition Matrix)

4. All of the above

5. None of the above