solbeta {bayess}R Documentation

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))

[Package bayess version 1.4 Index]