Chapter 12 "Decision theory", after completing this chapter, you should be able to: Outline the characteristics of a decision theory approach to decision making; describe and give examples of decisions under certainty, risk, and complete uncertainty; make decisions using maximin, maximax, minimax regret, Hurwicz, equally likely, and expected value criteria and use Excel to solve problems involving these techniques;. | Chapter 12 Markov Analysis Part 3 Probabilistic Decision Models Learning Objectives Give examples of systems that may lend themselves to be analyzed by a Markov model. Explain the meaning of transition probabilities. Describe the kinds of system behaviors that Markov analysis pertains to. Use a tree diagram to analyze system behavior. Use matrix multiplication to analyze system behavior. Use an algebraic method to solve for steady-state probabilities. After completing this chapter, you should be able to: McGraw-Hill/Irwin 12– Learning Objectives (cont’d) Analyze absorbing states, namely accounts receivable, using a Markov model. List the assumptions of a Markov model. Use Excel to solve various problems pertaining to a Markov model. After completing this chapter, you should be able to: McGraw-Hill/Irwin 12– Characteristics of a Markov System It will operate or exist for a number of periods. In each period, the system can assume one of a number of states or conditions. The states are both mutually exclusive and collectively exhaustive. System changes between states from period to period can be described by transition probabilities, which remain constant. The probability of the system being in a given state in a particular period depends only on its state in the preceding period and the transition probabilities. It is independent of all earlier periods. McGraw-Hill/Irwin 12– Markov Analysis: Assumptions Markov Analysis Assumptions The probability that an item in the system either will change from one state (., Airport A) to another or remain in its current state is a function of the transition probabilities only. The transition probabilities remain constant. The system is a closed one; there will be no arrivals to the system or exits from the system. McGraw-Hill/Irwin 12– Table 12–1 Examples of Systems That May Be Described as Markov McGraw-Hill/Irwin 12– Table 12–2 Transition Probabilities for Car Rental Example McGraw-Hill/Irwin 12– . | Chapter 12 Markov Analysis Part 3 Probabilistic Decision Models Learning Objectives Give examples of systems that may lend themselves to be analyzed by a Markov model. Explain the meaning of transition probabilities. Describe the kinds of system behaviors that Markov analysis pertains to. Use a tree diagram to analyze system behavior. Use matrix multiplication to analyze system behavior. Use an algebraic method to solve for steady-state probabilities. After completing this chapter, you should be able to: McGraw-Hill/Irwin 12– Learning Objectives (cont’d) Analyze absorbing states, namely accounts receivable, using a Markov model. List the assumptions of a Markov model. Use Excel to solve various problems pertaining to a Markov model. After completing this chapter, you should be able to: McGraw-Hill/Irwin 12– Characteristics of a Markov System It will operate or exist for a number of periods. In each period, the system can assume one of a number of states or conditions. The .
