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  2. Stochastic matrix - Wikipedia

    en.wikipedia.org/wiki/Stochastic_matrix

    In mathematics, a stochastic matrix is a square matrix used to describe the transitions of a Markov chain. Each of its entries is a nonnegative real number representing a probability. [ 1][ 2]: 10 It is also called a probability matrix, transition matrix, substitution matrix, or Markov matrix. The stochastic matrix was first developed by Andrey ...

  3. Chebyshev's inequality - Wikipedia

    en.wikipedia.org/wiki/Chebyshev's_inequality

    The rule is often called Chebyshev's theorem, about the range of standard deviations around the mean, in statistics. The inequality has great utility because it can be applied to any probability distribution in which the mean and variance are defined. For example, it can be used to prove the weak law of large numbers.

  4. Posterior probability - Wikipedia

    en.wikipedia.org/wiki/Posterior_probability

    t. e. The posterior probability is a type of conditional probability that results from updating the prior probability with information summarized by the likelihood via an application of Bayes' rule. [ 1] From an epistemological perspective, the posterior probability contains everything there is to know about an uncertain proposition (such as a ...

  5. Pointwise mutual information - Wikipedia

    en.wikipedia.org/wiki/Pointwise_mutual_information

    Pointwise mutual information. In statistics, probability theory and information theory, pointwise mutual information ( PMI ), [ 1] or point mutual information, is a measure of association. It compares the probability of two events occurring together to what this probability would be if the events were independent. [ 2]

  6. Mahalanobis distance - Wikipedia

    en.wikipedia.org/wiki/Mahalanobis_distance

    The Mahalanobis distance is a measure of the distance between a point and a distribution , introduced by P. C. Mahalanobis in 1936. [1] The mathematical details of Mahalanobis distance has appeared in the Journal of The Asiatic Society of Bengal. [2] Mahalanobis's definition was prompted by the problem of identifying the similarities of skulls ...

  7. Covariance and correlation - Wikipedia

    en.wikipedia.org/wiki/Covariance_and_correlation

    correlation. so that. where E is the expected value operator. Notably, correlation is dimensionless while covariance is in units obtained by multiplying the units of the two variables. If Y always takes on the same values as X, we have the covariance of a variable with itself (i.e. ), which is called the variance and is more commonly denoted as ...

  8. Fermi's golden rule - Wikipedia

    en.wikipedia.org/wiki/Fermi's_golden_rule

    Fermi's golden rule. In quantum physics, Fermi's golden rule is a formula that describes the transition rate (the probability of a transition per unit time) from one energy eigenstate of a quantum system to a group of energy eigenstates in a continuum, as a result of a weak perturbation. This transition rate is effectively independent of time ...

  9. Inclusion–exclusion principle - Wikipedia

    en.wikipedia.org/wiki/Inclusion–exclusion...

    Inclusion–exclusion principle. In combinatorics, a branch of mathematics, the inclusion–exclusion principle is a counting technique which generalizes the familiar method of obtaining the number of elements in the union of two finite sets; symbolically expressed as. where A and B are two finite sets and | S | indicates the cardinality of a ...