solbeta {bayess} | R Documentation |
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(\alpha,\beta)
prior distribution by a divide-and-conquer scheme.
solbeta(a, b, c, prec = 10^(-3))
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 |
Since the mean \mu
of the beta distribution is known, there is a single free parameter
\alpha
to determine, since \beta=\alpha(1-\mu)/\mu
. The function solbeta
searches for
the corresponding value of \alpha
, starting with a precision of 1
and stopping
at the requested precision prec
.
alpha |
first coefficient of the beta distribution |
beta |
second coefficient of the beta distribution |
solbeta(.1,.5,.3,10^(-4))