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where the marginal, joint, and/or conditional probability density functions are denoted by with the appropriate subscript. This can be simplified as This can be simplified as
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).
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 joint information is equal to the mutual information plus the sum of all the marginal information (negative of the marginal entropies) for each particle coordinate. Boltzmann's assumption amounts to ignoring the mutual information in the calculation of entropy, which yields the thermodynamic entropy (divided by the Boltzmann constant).
In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable given that the value of another random variable is known. Here, information is measured in shannons , nats , or hartleys .
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If a taxpayer earned $60,000 in taxable income in 2024, that person’s marginal tax rate is 22%, which is the rate for annual incomes that top out at between $47,150 and $100,525 in the IRS tax ...
The summation can be interpreted as a weighted average, and consequently the marginal probability, (), is sometimes called "average probability"; [2] "overall probability" is sometimes used in less formal writings. [3] The law of total probability can also be stated for conditional probabilities: