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In the independent case, we may calculate all minterm probabilities from the probabilities of the basic events. These calculations are straightforward, but may be tedious and subject to errors. An m-function minprob calculates all minterm probabilities from the probabilities of the basic or generating sets. To arrange these as on a minterm map, m-function minmap reshapes the row matrix pm. When minterm probabilities are available, m-function ikn(P,k) calculates individual probabilities and ckn(P,k) calculates the probabilities that k or more occur. These provide the basis of a variety of useful calculations. The m-function imintest checks a vector of minterm probabilities for feasible size, determines the number of variables, and performs a check for independence.As in the nonindependent case, we may utilize the minterm expansion and the minterm probabilities to calculate the probabilities of Boolean combinations of events. However,it is frequently more efficient to manipulate the expressions for the Boolean combination to be a disjoint union of intersections.Various Matlab calculations on independent Boolean combinations are readily performed.

Matlab and independent classes

In the unit on Minterms , we show how to use minterm probabilities and minterm vectors to calculate probabilities of Boolean combinations of events. In Independence of Events we show that in theindependent case, we may calculate all minterm probabilities from the probabilities of the basic events. While these calculations are straightforward, they may be tedious andsubject to errors. Fortunately, in this case we have an m-function minprob which calculates all minterm probabilities from the probabilities of the basic or generating sets. This function uses the m-function mintable to set upthe patterns of p 's and q 's for the various minterms and then takes the products to obtain the set of minterm probabilities.

>>pm = minprob(0.1*[4 7 6])pm = 0.0720 0.1080 0.1680 0.2520 0.0480 0.0720 0.1120 0.1680

It may be desirable to arrange these as on a minterm map. For this we have an m-function minmap which reshapes the row matrix p m , as follows:

>>t = minmap(pm) t = 0.0720 0.1680 0.0480 0.11200.1080 0.2520 0.0720 0.1680
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Probability of occurrence of k of n independent events

In Example 2, we show how to use the m-functions mintable and csort to obtain the probability of the occurrence of k of n events, when minterm probabilities are available. In the case of an independent class, the minterm probabilities are calculated easilyby minprob, It is only necessary to specify the probabilities for the n basic events and the numbers k of events. The size of the class, hence the mintable, is determined, and the minterm probabilities are calculated by minprob. We have twouseful m-functions. If P is a matrix of the n individual event probabilities, and k is a matrix of integers less than or equal to n , then

function y = ikn ( P , k ) calculates individual probabilities that k of n occur

function y = ckn ( P , k ) calculates the probabilities that k or more occur

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Source:  OpenStax, Applied probability. OpenStax CNX. Aug 31, 2009 Download for free at http://cnx.org/content/col10708/1.6
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