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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. Likelihood function - Wikipedia

    en.wikipedia.org/wiki/Likelihood_function

    More generally, for each value of , we can calculate the corresponding likelihood. The result of such calculations is displayed in Figure 1. The result of such calculations is displayed in Figure 1. The integral of L {\textstyle {\mathcal {L}}} over [0, 1] is 1/3; likelihoods need not integrate or sum to one over the parameter space.

  4. Radial unit hypothesis - Wikipedia

    en.wikipedia.org/wiki/Radial_unit_hypothesis

    The Radial Unit Hypothesis (RUH) is a conceptual theory of cerebral cortex development, first described by Pasko Rakic. It states that the cerebral cortex develops during embryogenesis as an array of interacting cortical columns , or 'radial units', each of which originates from a transient stem cell layer called the ventricular zone , which ...

  5. 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.

  6. 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.

  7. Bayes factor - Wikipedia

    en.wikipedia.org/wiki/Bayes_factor

    The Bayes factor is a ratio of two competing statistical models represented by their evidence, and is used to quantify the support for one model over the other. [1] The models in question can have a common set of parameters, such as a null hypothesis and an alternative, but this is not necessary; for instance, it could also be a non-linear model compared to its linear approximation.

  8. Multivariate normal distribution - Wikipedia

    en.wikipedia.org/wiki/Multivariate_normal...

    The null hypothesis is that the data set is similar to the normal distribution, therefore a sufficiently small p-value indicates non-normal data. Multivariate normality tests include the Cox–Small test [ 33 ] and Smith and Jain's adaptation [ 34 ] of the Friedman–Rafsky test created by Larry Rafsky and Jerome Friedman .

  9. Marginal zone - Wikipedia

    en.wikipedia.org/wiki/Marginal_zone

    The marginal zone is the region at the interface between the non-lymphoid red pulp and the lymphoid white-pulp of the spleen. (Some sources consider it to be the part of red pulp which borders on the white pulp, while other sources consider it to be neither red pulp nor white pulp.) A marginal zone also exists in the lymphoid follicles of lymph ...