Probabilities.BinomPDF Method

Overload List

#SignatureDescription
1void BinomPDF(TMtxVecInt X, Int32 n, Double P, TDenseMtxVec Res)Binomial PDF (vectorized).
2Double BinomPDF(Int32 x, Int32 N, Double p)Binomial probability mass function (PMF).

Overload 1: void BinomPDF(TMtxVecInt X, Int32 n, Double P, TDenseMtxVec Res)

Binomial PDF (vectorized).

#NameDescription
1XDefines distribution domain, real vector or matrix with integer values on closec interval [0,n].
2nDefines number of trials. n must be a positive integer.
3PDefines success probability. p must lie on the [0,1] closed interval.
4ResAfter calculation stores the PDF calculated using X, n and p. Length and Complex properties of Res are adjusted automatically to match Length and Complex properties of X.

Result: stored in self (calling object)

Overload 2: Double BinomPDF(Int32 x, Int32 N, Double p)

Binomial probability mass function (PMF).

#NameDescription
1xFunction domain, integer on the closed interval [0,N].
2NNumber of trials
3N must be a positive integer.
4pSuccess probability
5p must lie on the closed interval [0,1].

Returns: Double - the binomial probability mass function (PMF). Returns NAN when p is outside [0,1], N<=0, or x>N.

Remarks:

Computes the binomial PMF, the probability of observing exactly x successes in N independent Bernoulli trials each with success probability p:

PMF(x| N,p)=binomNx p^x (1-p)^(N-x), x=0,1,...,N

Domain: x in {0,1,...,N}, N >= 1, p in [0,1]. For p not in [0,1], N <= 0 or x>N the result is NaN.

To recognize a situation that involves a binomial random variable, the following assumptions must be met:

  • The experiment consists of a fixed number, N, of Bernoulli trials that result in either success or failure.
  • The trials are identical and independent, so the success probability p remains the same from trial to trial.
  • The random variable x denotes the number of successes obtained in the N trials.
See Also: Probabilities.BinomCDF, Probabilities.BinomCDFInv, Probabilities.BernoulliPDF