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21 The Exponential Distribution - Queen's U

21 The Exponential DistributionFrom Discrete-Time to Continuous-Time:In Chapter 6 of the text we will be considering Markov processes in con-tinuous time. In a sense, we already have a very good understanding ofcontinuous-time Markov chains based on our theory for discrete-timeMarkov chains. For example, one way to describe a continuous-timeMarkov chain is to say that it is a discrete-time Markov chain, exceptthat we explicitly model the times between transitions with contin-uous, positive-valued random variables and we explicity consider theprocess at any timet, not just at transition single most important continuous Distribution for building andunderstanding continuous-time Markov chains is the Exponential dis- tribution , for reasons which we shall explore in this THE Exponential DISTRIBUTIONThe Exponential Distribution :A continuous random variableXis said to have an Exponential ( ) Distribution if it has probability density functionfX(x| ) ={ e xforx >00forx 0,where >0is called therateof the the study of continuous-time stochastic processes, the exponentialdistribution is usually used to model thetime until something hap-pens in the process.}

understanding continuous-time Markov chains is the exponential dis-tribution, for reasons which we shall explore in this lecture. 177. 178 21. THE EXPONENTIAL DISTRIBUTION The Exponential Distribution: A continuous random variable X is said to have an Exponential(λ)

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