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  2. Joint probability distribution - Wikipedia

    en.wikipedia.org/wiki/Joint_probability_distribution

    In the case of real-valued random variables, the joint distribution, as a particular multivariate distribution, may be expressed by a multivariate cumulative distribution function, or by a multivariate probability density function together with a multivariate probability mass function.

  3. Chapman–Kolmogorov equation - Wikipedia

    en.wikipedia.org/wiki/Chapman–Kolmogorov_equation

    Download as PDF; Printable version; ... be the joint probability density function of the values of the random variables f 1 to f n. Then, the Chapman–Kolmogorov ...

  4. Probability density function - Wikipedia

    en.wikipedia.org/wiki/Probability_density_function

    In probability theory, a probability density function (PDF), density function, or density of an absolutely continuous random variable, is a function whose value at any given sample (or point) in the sample space (the set of possible values taken by the random variable) can be interpreted as providing a relative likelihood that the value of the ...

  5. Multivariate normal distribution - Wikipedia

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

    If () is a general scalar-valued function of a normal vector, its probability density function, cumulative distribution function, and inverse cumulative distribution function can be computed with the numerical method of ray-tracing (Matlab code). [17]

  6. Convolution of probability distributions - Wikipedia

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

    The probability distribution of the sum of two or more independent random variables is the convolution of their individual distributions. The term is motivated by the fact that the probability mass function or probability density function of a sum of independent random variables is the convolution of their corresponding probability mass functions or probability density functions respectively.

  7. Copula (statistics) - Wikipedia

    en.wikipedia.org/wiki/Copula_(statistics)

    when the two marginal functions and the copula density function are known, then the joint probability density function between the two random variables can be calculated, or; when the two marginal functions and the joint probability density function between the two random variables are known, then the copula density function can be calculated.

  8. Buffon's needle problem - Wikipedia

    en.wikipedia.org/wiki/Buffon's_needle_problem

    Suppose l > t.In this case, integrating the joint probability density function, we obtain: = = (), where m(θ) is the minimum between ⁠ l / 2 ⁠ sinθ and ⁠ t / 2 ⁠.. Thus, performing the above integration, we see that, when l > t, the probability that the needle will cross at least one line is

  9. Joint probability density function - Wikipedia

    en.wikipedia.org/?title=Joint_probability...

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