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  2. Marginal distribution - Wikipedia

    en.wikipedia.org/wiki/Marginal_distribution

    In probability theory and statistics, the marginal distribution of a subset of a collection of random variables is the probability distribution of the variables contained in the subset. It gives the probabilities of various values of the variables in the subset without reference to the values of the other variables.

  3. Law of total probability - Wikipedia

    en.wikipedia.org/wiki/Law_of_total_probability

    In probability theory, the law (or formula) of total probability is a fundamental rule relating marginal probabilities to conditional probabilities. It expresses the total probability of an outcome which can be realized via several distinct events , hence the name.

  4. Joint probability distribution - Wikipedia

    en.wikipedia.org/wiki/Joint_probability_distribution

    Moreover, the final row and the final column give the marginal probability distribution for A and the marginal probability distribution for B respectively. For example, for A the first of these cells gives the sum of the probabilities for A being red, regardless of which possibility for B in the column above the cell occurs, as ⁠ 2 / 3 ⁠ .

  5. Distribution of the product of two random variables - Wikipedia

    en.wikipedia.org/wiki/Distribution_of_the...

    We find the desired probability density function by taking the derivative of both sides with respect to . Since on the right hand side, appears only in the integration limits, the derivative is easily performed using the fundamental theorem of calculus and the chain rule. (Note the negative sign that is needed when the variable occurs in the ...

  6. Copula (statistics) - Wikipedia

    en.wikipedia.org/wiki/Copula_(statistics)

    In probability theory and statistics, a copula is a multivariate cumulative distribution function for which the marginal probability distribution of each variable is uniform on the interval [0, 1]. Copulas are used to describe/model the dependence (inter-correlation) between random variables. [1]

  7. Gibbs sampling - Wikipedia

    en.wikipedia.org/wiki/Gibbs_sampling

    Gibbs sampling is named after the physicist Josiah Willard Gibbs, in reference to an analogy between the sampling algorithm and statistical physics.The algorithm was described by brothers Stuart and Donald Geman in 1984, some eight decades after the death of Gibbs, [1] and became popularized in the statistics community for calculating marginal probability distribution, especially the posterior ...

  8. What Is the Marginal vs. Effective Tax Rate? - AOL

    www.aol.com/marginal-vs-effective-tax-rate...

    Yes. It's easy to calculate your marginal tax rate without professional assistance. In fact, it doesn't require any calculation at all. Simply determine your combined taxable income and find the ...

  9. Conditional probability distribution - Wikipedia

    en.wikipedia.org/wiki/Conditional_probability...

    Given two jointly distributed random variables and , the conditional probability distribution of given is the probability distribution of when is known to be a particular value; in some cases the conditional probabilities may be expressed as functions containing the unspecified value of as a parameter.

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