k successful trials.
@param k Number of successful trials
@return Math.exp(binomialCoefficientLn(nExp, k) + k * logP + (nExp-k) * logOneMinusP);
x and y which can be correlated in a particular manner.
@param x First variable
@param y Second variable
@return BVN
Computes the Cumulative Binomial Distribution. @param k @return 1.0 - Beta.incompleteBetaFunction(k+1, n_-k, p_, accuracy, maxIteration)
Asymptotic expansion for very negative z as references on M. Abramowitz book. @see cite: "Monte Carlo Methods in Finance", ISBN-13: 978-0471497417 @see cite: M. Abramowitz and A. Stegun, Pocketbook of Mathematical Functions, ISBN 3-87144818-4, p.408, item 26.2.12 @param z @return result
Computes the cumulative Poisson distribution by using the incomplete gamma function . @param k is the number of occurrences of an event @return the cumulative Poisson distribution by using the incomplete gamma function
x
average + z * sigma
x
Computes the Normal distribution at point {@latex$ x } @param x @return the Normal distribution at point {@latex$ x }
PoissonDistribution evaluation
Compute the Poisson Distribution with input {@latex$ \mu} and {@latex$ k}. @param k @return Math.exp(k*Math.log(mu_) - logFactorial - mu_)
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