lambda determines the
difficulty of changing topologies. There are two
options in the program.
In the run settings menu you are
asked if you want to update lambda.
lambda is kept fixed.
You are prompted for this fixed value in the
model options menu.
lambda is sampled with
Gibbs sampling from the posterior distribution,
which has the form of a beta distribution.
You are again
prompted for a value of lambda in the
model options menu. This value, however,
is not the value of lambda itself, but the
mean of its prior distribution.
The beta distribution depends on two parameters,
alpha and beta.
These parameters are related to the mean according to
mean = alpha/(alpha+beta).
In the program, the value of beta
is kept fixed: beta=2.
Specifying the mean just determines both parameters of
the prior distribution.
Note, however, that as the mean increases, the variance of
the prior distribution decreases.
The following figure shows plots of the prior distribution
for various values of the mean, as shown at the top
of each subfigure.
Last modified: July 2001
Back to the beginning of this documentation.