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function L = log_marg_prob_node(CPD, self_ev, pev, usecell)
% LOG_MARG_PROB_NODE Compute sum_m log P(x(i,m)| x(pi_i,m)) for node i (tabular)
% L = log_marg_prob_node(CPD, self_ev, pev)
%
% This differs from log_prob_node because we integrate out the parameters.
% self_ev(m) is the evidence on this node in case m.
% pev(i,m) is the evidence on the i'th parent in case m (if there are any parents).
% (These may also be cell arrays.)
ncases = length(self_ev);
sz = CPD.sizes;
nparents = length(sz)-1;
assert(ncases == size(pev, 2));
if nargin < 4
%usecell = 0;
if iscell(self_ev)
usecell = 1;
else
usecell = 0;
end
end
if ncases==0
L = 0;
return;
elseif ncases==1 % speedup the sequential learning case
CPT = CPD.CPT;
% We assume the CPTs are already set to the mean of the posterior (due to bayes_update_params)
if usecell
x = cat(1, pev{:})';
y = self_ev{1};
else
%x = pev(:)';
x = pev;
y = self_ev;
end
switch nparents
case 0, p = CPT(y);
case 1, p = CPT(x(1), y);
case 2, p = CPT(x(1), x(2), y);
case 3, p = CPT(x(1), x(2), x(3), y);
otherwise,
ind = subv2ind(sz, [x y]);
p = CPT(ind);
end
L = log(p);
else
% We ignore the CPTs here and assume the prior has not been changed
% We arrange the data as in the following example.
% Let there be 2 parents and 3 cases. Let p(i,m) be parent i in case m,
% and y(m) be the child in case m. Then we create the data matrix
%
% p(1,1) p(1,2) p(1,3)
% p(2,1) p(2,2) p(2,3)
% y(1) y(2) y(3)
if usecell
data = [cell2num(pev); cell2num(self_ev)];
else
data = [pev; self_ev];
end
%S = struct(CPD); fprintf('log marg prob node %d, ps\n', S.self); disp(S.parents)
counts = compute_counts(data, sz);
L = dirichlet_score_family(counts, CPD.dirichlet);
end
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