Search results
Results from the WOW.Com Content Network
The joint distribution encodes the marginal distributions, i.e. the distributions of each of the individual random variables and the conditional probability distributions, which deal with how the outputs of one random variable are distributed when given information on the outputs of the other random variable(s).
In microeconomics, joint product pricing is the firm's problem of choosing prices for joint products, which are two or more products produced from the same process or operation, each considered to be of value. Pricing for joint products is more complex than pricing for a single product. To begin with, there are two demand curves.
Joint and marginal distributions of a pair of discrete random variables, X and Y, dependent, thus having nonzero mutual information I(X; Y). The values of the joint distribution are in the 3×4 rectangle; the values of the marginal distributions are along the right and bottom margins.
The conditional distribution contrasts with the marginal distribution of a random variable, which is its distribution without reference to the value of the other variable. If the conditional distribution of Y {\displaystyle Y} given X {\displaystyle X} is a continuous distribution , then its probability density function is known as the ...
The squared Mahalanobis distance () is decomposed into a sum of k terms, each term being a product of three meaningful components. [6] Note that in the case when k = 1 {\displaystyle k=1} , the distribution reduces to a univariate normal distribution and the Mahalanobis distance reduces to the absolute value of the standard score .
The graph of the log-likelihood is called the support curve (in the univariate case). [36] In the multivariate case, the concept generalizes into a support surface over the parameter space . It has a relation to, but is distinct from, the support of a distribution .
For example, it may be used, when joint probability density function between two random variables is known, the copula density function is known, and one of the two marginal functions are known, then, the other marginal function can be calculated, or
A popular message passing algorithm on factor graphs is the sum–product algorithm, which efficiently computes all the marginals of the individual variables of the function. In particular, the marginal of variable X k {\displaystyle X_{k}} is defined as