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  2. Relationships among probability distributions - Wikipedia

    en.wikipedia.org/wiki/Relationships_among...

    If X 1 is a normal (μ 1, σ 2 1) random variable and X 2 is a normal (μ 2, σ 2 2) random variable, then X 1 + X 2 is a normal (μ 1 + μ 2, σ 2 1 + σ 2 2) random variable. The sum of N chi-squared (1) random variables has a chi-squared distribution with N degrees of freedom. Other distributions are not closed under convolution, but their ...

  3. Sum of normally distributed random variables - Wikipedia

    en.wikipedia.org/wiki/Sum_of_normally...

    then. This means that the sum of two independent normally distributed random variables is normal, with its mean being the sum of the two means, and its variance being the sum of the two variances (i.e., the square of the standard deviation is the sum of the squares of the standard deviations). [1]

  4. Normal distribution - Wikipedia

    en.wikipedia.org/wiki/Normal_distribution

    Probability theory. In probability theory and statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode ), while ...

  5. 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 ...

  6. Hidden Markov model - Wikipedia

    en.wikipedia.org/wiki/Hidden_Markov_model

    Each oval shape represents a random variable that can adopt any of a number of values. The random variable x(t) is the hidden state at time t (with the model from the above diagram, x(t) ∈ { x 1, x 2, x 3 }). The random variable y(t) is the observation at time t (with y(t) ∈ { y 1, y 2, y 3, y 4 }).

  7. Convolution of probability distributions - Wikipedia

    en.wikipedia.org/wiki/Convolution_of_probability...

    The convolution/sum of probability distributions arises in probability theory and statistics as the operation in terms of probability distributions that corresponds to the addition of independent random variables and, by extension, to forming linear combinations of random variables. The operation here is a special case of convolution in the ...

  8. State switching - Wikipedia

    en.wikipedia.org/wiki/State_switching

    State switching (a.k.a. phenotypic switching) is a fundamental physiological process in which a cell/organism undergoes spontaneous, and potentially reversible, transitions between different phenotypes. Thus, the ability to switch states/phenotypes ( phenotypic plasticity) is a key feature of development and normal function of cells within most ...

  9. Algebra of random variables - Wikipedia

    en.wikipedia.org/wiki/Algebra_of_random_variables

    The algebra of random variables in statistics, provides rules for the symbolic manipulation of random variables, while avoiding delving too deeply into the mathematically sophisticated ideas of probability theory. Its symbolism allows the treatment of sums, products, ratios and general functions of random variables, as well as dealing with ...