egttools.distributions.multivariate_hypergeometric_pdf

multivariate_hypergeometric_pdf(*args, **kwargs)

Overloaded function.

  1. multivariate_hypergeometric_pdf(m: int, k: int, n: int, sample_counts: List[int], population_counts: numpy.ndarray[numpy.uint64[m, 1]]) -> float

    Calculates the probability density function of a multivariate hyper-geometric distribution.

    This function returns the probability that a sample :param sample_counts would be drawn from a population :param population_counts. Assuming that the population is of size :param m, has :param k objects, and the sample has size :param n.

    Both :param population_counts and :param sample_counts must be of shape (k,). The sum of all entries in :param population_counts, must sum to :param m, and the sum of all entries in :param sample_counts must sum to :param n.

    For the application often used in this library, :param m would be the size of the population, :param k would be the number of strategies, :param n would be the group size, :param sample_counts would contain the counts of each strategy in the group, and :param population_counts contains the counts of each strategy in the population.

    mint

    size of the population

    kint

    number of objects in the population

    nint

    size of the sample

    sample_countsList[int]

    a vector containing the counts of each objects in the sample

    population_countsnumpy.ndarray

    a vector containing the counts of each objects in the population

    float

    The probability that a sample of size n in a population of k objects

    egttools.distributions.binom egttools.distributions.comb

  2. multivariate_hypergeometric_pdf(m: int, k: int, n: int, sample_counts: numpy.ndarray[numpy.uint64[m, 1]], population_counts: numpy.ndarray[numpy.uint64[m, 1]]) -> float

    Calculates the probability density function of a multivariate hyper-geometric distribution.

    This function returns the probability that a sample :param sample_counts would be drawn from a population :param population_counts. Assuming that the population is of size :param m, has :param k objects, and the sample has size :param n.

    Both :param population_counts and :param sample_counts must be of shape (k,). The sum of all entries in :param population_counts, must sum to :param m, and the sum of all entries in :param sample_counts must sum to :param n.

    For the application often used in this library, :param m would be the size of the population, :param k would be the number of strategies, :param n would be the group size, :param sample_counts would contain the counts of each strategy in the group, and :param population_counts contains the counts of each strategy in the population.

    mint

    size of the population

    kint

    number of objects in the population

    nint

    size of the sample

    sample_countsList[int]

    a vector containing the counts of each objects in the sample

    population_countsnumpy.ndarray

    a vector containing the counts of each objects in the population

    float

    The probability that a sample of size n in a population of k objects

    egttools.distributions.binom egttools.distributions.comb