probPOS: Bayesian posterior Probabilities

In proportion: Inference on Single Binomial Proportion and Bayesian Computations

Description

Bayesian posterior Probabilities

Usage

probPOS(n, a, b, th)

Arguments

n	- Number of trials
a	- Prior Parameters
b	- Prior Parameters
th	- Theta value seeking Pr(Theta/X < th)

Details

Computes probability of the event p < p0 (p0 is specified in [0, 1]) based on posterior distribution of Beta-Binomial model with given parameters for prior Beta distribution for all x = 0, 1, 2......n where n is the number of trials

Value

A dataframe with

x	Number of successes
PosProb	Posterior probability

References

[1] 2002 Gelman A, Carlin JB, Stern HS and Dunson DB Bayesian Data Analysis, Chapman & Hall/CRC [2] 2006 Ghosh M, Delampady M and Samanta T. An introduction to Bayesian analysis: Theory and Methods. Springer, New York

See Also

Other Miscellaneous functions for Bayesian method: empericalBAx, empericalBA, probPOSx, probPREx, probPRE

Examples

n=5;  a=0.5; b=0.5; th=0.5;
probPOS(n,a,b,th)

Example output

  x     PosProb
1 0 0.993127696
2 1 0.912287200
3 2 0.669765259
4 3 0.330234727
5 4 0.087712909
6 5 0.006872303

proportion documentation built on May 1, 2019, 7:54 p.m.