nag_prob_poisson_vector (g01skc) (PDF version)
g01 Chapter Contents
g01 Chapter Introduction
NAG Library Manual

NAG Library Function Document

nag_prob_poisson_vector (g01skc)

+ Contents

    1  Purpose
    7  Accuracy

1  Purpose

nag_prob_poisson_vector (g01skc) returns a number of the lower tail, upper tail and point probabilities for the Poisson distribution.

2  Specification

#include <nag.h>
#include <nagg01.h>
void  nag_prob_poisson_vector (Integer ll, const double l[], Integer lk, const Integer k[], double plek[], double pgtk[], double peqk[], Integer ivalid[], NagError *fail)

3  Description

Let X = Xi: i=1 , 2 ,, m  denote a vector of random variables each having a Poisson distribution with parameter λi >0. Then
Prob Xi = ki = e -λi λi ki ki! ,   ki = 0,1,2,
The mean and variance of each distribution are both equal to λi.
nag_prob_poisson_vector (g01skc) computes, for given λi and ki the probabilities: ProbXiki, ProbXi>ki and ProbXi=ki using the algorithm described in Knüsel (1986).
The input arrays to this function are designed to allow maximum flexibility in the supply of vector arguments by re-using elements of any arrays that are shorter than the total number of evaluations required. See Section 2.6 in the g01 Chapter Introduction for further information.

4  References

Knüsel L (1986) Computation of the chi-square and Poisson distribution SIAM J. Sci. Statist. Comput. 7 1022–1036

5  Arguments

1:     llIntegerInput
On entry: the length of the array l.
Constraint: ll>0.
2:     l[ll]const doubleInput
On entry: λi, the parameter of the Poisson distribution with λi=l[j], j=i-1 mod ll, for i=1,2,,maxll,lk.
Constraint: 0.0<l[j-1]106, for j=1,2,,ll.
3:     lkIntegerInput
On entry: the length of the array k.
Constraint: lk>0.
4:     k[lk]const IntegerInput
On entry: ki, the integer which defines the required probabilities with ki=k[j], j=i-1 mod lk.
Constraint: k[j-1]0, for j=1,2,,lk.
5:     plek[dim]doubleOutput
Note: the dimension, dim, of the array plek must be at least maxll,lk.
On exit: Prob Xi ki , the lower tail probabilities.
6:     pgtk[dim]doubleOutput
Note: the dimension, dim, of the array pgtk must be at least maxll,lk.
On exit: Prob Xi > ki , the upper tail probabilities.
7:     peqk[dim]doubleOutput
Note: the dimension, dim, of the array peqk must be at least maxll,lk.
On exit: Prob Xi = ki , the point probabilities.
8:     ivalid[dim]IntegerOutput
Note: the dimension, dim, of the array ivalid must be at least maxll,lk.
On exit: ivalid[i-1] indicates any errors with the input arguments, with
ivalid[i-1]=0
No error.
ivalid[i-1]=1
On entry,λi0.0.
ivalid[i-1]=2
On entry,ki<0.
ivalid[i-1]=3
On entry,λi>106.
9:     failNagError *Input/Output
The NAG error argument (see Section 3.6 in the Essential Introduction).

6  Error Indicators and Warnings

NE_ALLOC_FAIL
Dynamic memory allocation failed.
NE_ARRAY_SIZE
On entry, array size=value.
Constraint: lk>0.
On entry, array size=value.
Constraint: ll>0.
NE_BAD_PARAM
On entry, argument value had an illegal value.
NE_INTERNAL_ERROR
An internal error has occurred in this function. Check the function call and any array sizes. If the call is correct then please contact NAG for assistance.
NW_IVALID
On entry, at least one value of l or k was invalid.
Check ivalid for more information.

7  Accuracy

Results are correct to a relative accuracy of at least 10-6 on machines with a precision of 9 or more decimal digits (provided that the results do not underflow to zero).

8  Parallelism and Performance

Not applicable.

9  Further Comments

The time taken by nag_prob_poisson_vector (g01skc) to calculate each probability depends on λi and ki. For given λi, the time is greatest when kiλi, and is then approximately proportional to λi.

10  Example

This example reads a vector of values for λ and k, and prints the corresponding probabilities.

10.1  Program Text

Program Text (g01skce.c)

10.2  Program Data

Program Data (g01skce.d)

10.3  Program Results

Program Results (g01skce.r)


nag_prob_poisson_vector (g01skc) (PDF version)
g01 Chapter Contents
g01 Chapter Introduction
NAG Library Manual

© The Numerical Algorithms Group Ltd, Oxford, UK. 2014