Transcription of CS 547 Lecture 34: Markov Chains
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CS 547 Lecture 34: Markov Chains
CS 547 Lecture 34: Markov ChainsDaniel MyersState Transition ModelsA Markov chain is a model consisting of a group ofstatesand specifiedtransitionsbetween the states. Oldertexts on queueing theory prefer to derive most of their results using Markov models, as opposed to the meanvalue analysis approach we ve used for most of this course. Understanding Markov Chains will allow us toderive some new results that would be difficult to get using MVA of Markov ChainsA Markov chain can have a finite or infinite number of states. In adiscrete time Markov chain (DTMC) eachstate change takes place at a fixed decision point and the time between changes is constant. In acontinuoustime Markov chain (CTMC), changes can happen at any you might expect, finite and discrete models are easier to analyze, so we ll use a DTMC for our examplesin this Lecture .
CS 547 Lecture 34: Markov Chains Daniel Myers State Transition Models A Markov chain is a model consisting of a group of states and specified transitions between the states. Older texts on queueing theory prefer to derive most of their results using Markov models, as opposed to the mean
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