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
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 |
Details
Since the mean of the beta distribution is known, there is a single free parameter
to determine, since
. The function
solbeta
searches for
the corresponding value of , starting with a precision of
and stopping
at the requested precision
prec
.
Value
alpha |
first coefficient of the beta distribution |
beta |
second coefficient of the beta distribution |
See Also
Examples
solbeta(.1,.5,.3,10^(-4))
[Package bayess version 1.6 Index]