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function [ bnet ] = parameterLearning( bnet,cases,engine_name )
%parameterLearning Do parameter learning and inference
% It returns the bnet with parameters learned from the data in cases.
%
% This is very basic now. It could be modified to use different engine
% types in the future. Now, I always use the 'jtree_inf_engine'.
% with dirichlet priors
%
% parameterLearning is called by runBN_initial.m,
% Predictmultiple.m, and Predictmultipleintervention.m
%engine is an optional argument
if nargin < 3
engine_name = 'jtree_inf_engine';
end
%First do parameter learning with all the data
[bnet] = getParams(bnet,cases);
end
function [ bnet ] = getParams( bnet, cases )
%getParams Code to initialize CPT and do parameter learning.
%This will be very basic for now. I can add more options later.
%
dnodes = bnet.dnodes;
cnodes = bnet.cnodes;
nnodes = size(dnodes,2)+size(cnodes,2);
%make dnodes tabular_CPT
for i = 1:size(dnodes,2)
% bnet.CPD{dnodes(i)} = tabular_CPD(bnet,dnodes(i));
bnet.CPD{dnodes(i)} = tabular_CPD(bnet,dnodes(i),'prior_type','dirichlet');
end
for i = 1:size(cnodes,2)
bnet.CPD{cnodes(i)} = gaussian_CPD(bnet,cnodes(i));
end
bnet = learn_params(bnet,cases);
end
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