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  2. Marginal distribution - Wikipedia

    en.wikipedia.org/wiki/Marginal_distribution

    The marginal probability P(H = Hit) is the sum 0.572 along the H = Hit row of this joint distribution table, as this is the probability of being hit when the lights are red OR yellow OR green. Similarly, the marginal probability that P(H = Not Hit) is the sum along the H = Not Hit row.

  3. Marginal likelihood - Wikipedia

    en.wikipedia.org/wiki/Marginal_likelihood

    A marginal likelihood is a likelihood function that has been integrated over the parameter space.In Bayesian statistics, it represents the probability of generating the observed sample for all possible values of the parameters; it can be understood as the probability of the model itself and is therefore often referred to as model evidence or simply evidence.

  4. 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.

  5. Empirical Bayes method - Wikipedia

    en.wikipedia.org/wiki/Empirical_Bayes_method

    Empirical Bayes, also known as maximum marginal likelihood, [2] represents a convenient approach for setting hyperparameters, but has been mostly supplanted by fully Bayesian hierarchical analyses since the 2000s with the increasing availability of well-performing computation techniques.

  6. Principle of marginality - Wikipedia

    en.wikipedia.org/wiki/Principle_of_marginality

    In statistics, the principle of marginality, sometimes called hierarchical principle, is the fact that the average (or main) effects of variables in an analysis are marginal to their interaction effect—that is, the main effect of one explanatory variable captures the effect of that variable averaged over all values of a second explanatory variable whose value influences the first variable's ...

  7. 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 ]

  8. Fed officials signal more gradual approach to lowering rates ...

    www.aol.com/finance/fed-officials-signal-more...

    Federal Reserve governor Lisa Cook said Monday it makes sense to lower interest rates more gradually given resilience in the job market and stickier-than-expected inflation, the latest central ...

  9. 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 ⁠ .