solbeta: Recursive resolution of beta prior calibration

In bayess: Bayesian Essentials with R

Recursive resolution of beta prior calibration

Description

In the capture-recapture experiment of Chapter 5, the prior information is represented by a prior expectation and prior confidence intervals. This function derives the corresponding beta B(α,β) prior distribution by a divide-and-conquer scheme.

Usage

solbeta(a, b, c, prec = 10^(-3))

Arguments

a	lower bound of the prior 95%~confidence interval
b	upper bound of the prior 95%~confidence interval
c	mean of the prior distribution
prec	maximal precision on the beta coefficient α

Details

Since the mean μ of the beta distribution is known, there is a single free parameter α to determine, since β=α(1-μ)/μ. The function solbeta searches for the corresponding value of α, starting with a precision of 1 and stopping at the requested precision prec.

Value

alpha	first coefficient of the beta distribution
beta	second coefficient of the beta distribution

See Also

probet

Examples

solbeta(.1,.5,.3,10^(-4))

bayess documentation built on Aug. 11, 2022, 5:07 p.m.