Probabilities.BinomCDF Method

Overload List

#SignatureDescription
1procedure BinomCDF(const X: TMtxVecInt; n: Integer; P: Double; const Res: TDenseMtxVec);Binomial CDF (vectorized).
2function BinomCDF(x: Integer; N: Integer; p: Double): Double;Binomial cumulative distribution function (CDF).

Overload 1: procedure BinomCDF(const X: TMtxVecInt; n: Integer; P: Double; const Res: TDenseMtxVec);

Binomial CDF (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 CDF 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: function BinomCDF(x: Integer; N: Integer; p: Double): Double;

Binomial cumulative distribution function (CDF).

#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 cumulative distribution function (CDF). Returns NAN when p is outside [0,1], N<=0, or x>N.

Remarks:

Computes the binomial CDF, the probability of observing up to x successes in N independent trials with success probability p:

CDF(x| N,p)=sum_(j=0)^xbinomNj p^j (1-p)^(N-j)=I_(1-p)(N-x, x+1)

where I_z(a,b) is the regularized incomplete beta function (the implementation evaluates 1-I_p(x+1,N-x)). Domain: x in {0,...,N}, N >= 1, p in [0,1]; otherwise the result is NaN.

See Also: Probabilities.BinomPDF, Probabilities.BinomCDFInv, Probabilities.BernoulliCDF