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1989
The work in Part II was concerned mostly with one random variable. Actual experiments usually involve more than one outcome; an obvious case is the yield of a corn field which depends on (the random) rainfall, ground conditions, sunlight even though (the non–random) seed, fertilizer, irrigation might be used.
Hung T. Nguyen, Gerald S. Rogers
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The work in Part II was concerned mostly with one random variable. Actual experiments usually involve more than one outcome; an obvious case is the yield of a corn field which depends on (the random) rainfall, ground conditions, sunlight even though (the non–random) seed, fertilizer, irrigation might be used.
Hung T. Nguyen, Gerald S. Rogers
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Joint Probability Distributions
2011In Chapters 3 and 4, we studied probability models for a single random variable. Many problems in probability and statistics lead to models involving several random variables simultaneously. In this chapter, we first discuss probability models for the joint behavior of several random variables, putting special emphasis on the case in which the ...
Jay L. Devore, Kenneth N. Berk
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Continuous Joint Distributions
1993The joint distribution of a pair of random variables X and Y is the probability distribution over the plane defined by $$P\left( B \right) = P\left( {\left( {X,Y} \right) \in B} \right)$$ for subsets B of the plane. So P(B) is the probability that the random pair (X, Y) falls in the set B.
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Applications of Joint Distributions
2003This Chapter presents two applications of joint fuzzy probability distributions discussed in Chapter 10. In the next section we have an application of a joint fuzzy discrete probability distribution followed by a discussion of fuzzy reliability theory which is an application of joint fuzzy continuous probability distribution.
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Integrative oncology: Addressing the global challenges of cancer prevention and treatment
Ca-A Cancer Journal for Clinicians, 2022Jun J Mao,, Msce +2 more
exaly
L’American Joint Distribution Committee [“Joint”]
Le Monde Juif, 1964M. Brener, J. Jefroykin
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Joint Fuzzy Probability Distributions
2003This Chapter generalizes Chapters 4 and 8 to multivariable fuzzy probability distributions. Since the discrete case and the continuous case are similar, just interchange summation and integrals symbols, we only present the continuous case in Section 10.2. Applications are in the next Chapter. The continuous case is based on [1].
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