Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/Poisson_Theorem.R

Given n Bernoulli experiments, with success probability p (p small), this function calculates the approximate probability that a successful event occurs exactly m times.

1 | ```
Poisson_Theorem(n, m, p)
``` |

`n` |
An integer value representing the number of repetitions of the Bernoulli experiment. |

`m` |
An integer value representing the number of times that a successful event occurs in the n repetitions of the Bernoulli experiment. |

`p` |
A real value with the probability that a successful event will happen in any single Bernoulli experiment (called the probability of success). |

Bernoulli experiments are sequences of events, in which successive experiments are independent and at each experiment the probability of appearance of a "successful" event (p) remains constant. The value of n must be high and the value of p must be very small.

A numerical value representing the approximate probability that a successful event occurs exactly m times.

Department of Mathematics. University of Oriente. Cuba.

Larisa Zamora and Jorge Diaz

Gnedenko, B. V. (1978). The Theory of Probability. Mir Publishers. Moscow.

Integral_Theorem, Local_Theorem.

1 2 3 4 5 6 7 8 9 10 | ```
Prob<-Poisson_Theorem(n=100,m=50,p=0.002)
Prob
## The function is currently defined as
function (n, m, p)
{
landa <- n * p
P <- dpois(m, landa)
return(P)
}
``` |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.