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  2. Conjugate prior - Wikipedia

    en.wikipedia.org/wiki/Conjugate_prior

    In Bayesian probability theory, if, given a likelihood function (), the posterior distribution is in the same probability distribution family as the prior probability distribution (), the prior and posterior are then called conjugate distributions with respect to that likelihood function and the prior is called a conjugate prior for the likelihood function ().

  3. Prior probability - Wikipedia

    en.wikipedia.org/wiki/Prior_probability

    An informative prior expresses specific, definite information about a variable. An example is a prior distribution for the temperature at noon tomorrow. A reasonable approach is to make the prior a normal distribution with expected value equal to today's noontime temperature, with variance equal to the day-to-day variance of atmospheric temperature, or a distribution of the temperature for ...

  4. Dirichlet distribution - Wikipedia

    en.wikipedia.org/wiki/Dirichlet_distribution

    The Dirichlet distribution is the conjugate prior distribution of the categorical distribution (a generic discrete probability distribution with a given number of possible outcomes) and multinomial distribution (the distribution over observed counts of each possible category in a set of categorically distributed observations).

  5. Maximum a posteriori estimation - Wikipedia

    en.wikipedia.org/wiki/Maximum_a_posteriori...

    Analytically, when the mode(s) of the posterior density can be given in closed form. This is the case when conjugate priors are used. Via numerical optimization such as the conjugate gradient method or Newton's method. This usually requires first or second derivatives, which have to be evaluated analytically or numerically.

  6. Bayesian linear regression - Wikipedia

    en.wikipedia.org/wiki/Bayesian_linear_regression

    Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables, with the goal of obtaining the posterior probability of the regression coefficients (as well as other parameters describing the distribution of the regressand) and ultimately allowing the out-of-sample prediction of the regressand (often ...

  7. Convex conjugate - Wikipedia

    en.wikipedia.org/wiki/Convex_conjugate

    In mathematics and mathematical optimization, the convex conjugate of a function is a generalization of the Legendre transformation which applies to non-convex functions. It is also known as Legendre–Fenchel transformation , Fenchel transformation , or Fenchel conjugate (after Adrien-Marie Legendre and Werner Fenchel ).

  8. Complex conjugate - Wikipedia

    en.wikipedia.org/wiki/Complex_conjugate

    In mathematics, the complex conjugate of a complex number is the number with an equal real part and an imaginary part equal in magnitude but opposite in sign. That is, if a {\displaystyle a} and b {\displaystyle b} are real numbers, then the complex conjugate of a + b i {\displaystyle a+bi} is a − b i . {\displaystyle a-bi.}

  9. Conjugacy class - Wikipedia

    en.wikipedia.org/wiki/Conjugacy_class

    In mathematics, especially group theory, two elements and of a group are conjugate if there is an element in the group such that =. This is an equivalence relation whose equivalence classes are called conjugacy classes .