prob attribute for each node, the
joint probability distribution is derived. Then the quantities needed in
the local master procedure for finding the local parameter priors are
deduced.jointprior(nw,N=NA,phiprior="bottcher",timetrace=FALSE)
jointdisc(nw,timetrace=FALSE)
jointcont(nw,timetrace=FALSE)prob
attribute to describe the local probability distribution, see
network.N is given, the procedure tries to
set a value as low as possible.phiprior="bottcher" or phiprior="heckerman" can be used.TRUE, prints some timing information on the
screen.jointalpha is determined by multiplying
each entry in the joint probability distribution by the size of the
imaginary data base N.
For the mixed part of the network, for each configuration of the discrete
variables, the joint (Gaussian) distribution of the continuous
variables is constructed and represented by jointmu (one
row for each configuration of the discrete parents) and
jointsigma (a list of matrices -- one for each configuration of
the discrete parents). The configurations of the discrete parents are
ordered according to findex. The algorithm for
constructing the joint distribution of the continuous variables is
described in e.g. Shachter and Kenley (1989).
Then, jointalpha, jointnu, jointrho, mu and
jointphi are deduced. These quantities are later used for
deriving local parameter priors.
For the configuration i of the discrete variables,
$$\nu_i=\rho_i=\alpha_i$$ and
$$\phi_i = (\nu_i -1)\Sigma_i$$
if phiprior="bottcher" and
$$\phi_i = \nu_i(\rho_i -2)\Sigma_i/(\nu_i+1)$$
if phiprior="heckerman".networkdata(rats)
rats.nw <- network(rats)
rats.prior <- jointprior(rats.nw,12)Run the code above in your browser using DataLab