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  2. Conditional probability table - Wikipedia

    en.wikipedia.org/wiki/Conditional_probability_table

    The first column sum is the probability that x =0 and y equals any of the values it can have – that is, the column sum 6/9 is the marginal probability that x=0. If we want to find the probability that y=0 given that x=0, we compute the fraction of the probabilities in the x=0 column that have the value y=0, which is 4/9 ÷ 6/9 = 4/6. Likewise ...

  3. Conditional probability distribution - Wikipedia

    en.wikipedia.org/wiki/Conditional_probability...

    Seen as a function of for given , (= | =) is a probability mass function and so the sum over all (or integral if it is a conditional probability density) is 1. Seen as a function of x {\displaystyle x} for given y {\displaystyle y} , it is a likelihood function , so that the sum (or integral) over all x {\displaystyle x} need not be 1.

  4. Conditional probability - Wikipedia

    en.wikipedia.org/wiki/Conditional_probability

    The resulting limit is the conditional probability distribution of Y given X and exists when the denominator, the probability density (), is strictly positive. It is tempting to define the undefined probability P ( A ∣ X = x ) {\displaystyle P(A\mid X=x)} using limit ( 1 ), but this cannot be done in a consistent manner.

  5. Identity line - Wikipedia

    en.wikipedia.org/wiki/Identity_line

    The Keynesian cross diagram includes an identity line to show states in which aggregate demand equals output. In a 2-dimensional Cartesian coordinate system, with x representing the abscissa and y the ordinate, the identity line [1] [2] or line of equality [3] is the y = x line. The line, sometimes called the 1:1 line, has a slope of 1. [4]

  6. Chain rule (probability) - Wikipedia

    en.wikipedia.org/wiki/Chain_rule_(probability)

    This rule allows one to express a joint probability in terms of only conditional probabilities. [4] The rule is notably used in the context of discrete stochastic processes and in applications, e.g. the study of Bayesian networks, which describe a probability distribution in terms of conditional probabilities.

  7. Buffon's needle problem - Wikipedia

    en.wikipedia.org/wiki/Buffon's_needle_problem

    Here, x = 0 represents a needle that is centered directly on a line, and x = ⁠ t / 2 ⁠ represents a needle that is perfectly centered between two lines. The uniform PDF assumes the needle is equally likely to fall anywhere in this range, but could not fall outside of it. The uniform probability density function of θ between 0 and ⁠ π ...

  8. Microsoft Excel - Wikipedia

    en.wikipedia.org/wiki/Microsoft_Excel

    Subroutine in Excel calculates the square of named column variable x read from the spreadsheet, and writes it into the named column variable y. The Windows version of Excel supports programming through Microsoft's Visual Basic for Applications (VBA), which is a dialect of Visual Basic. Programming with VBA allows spreadsheet manipulation that ...

  9. 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.