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function [marginal, engine] = marginal_nodes(engine, nodes, varargin);
% MARGINAL_NODES Compute the marginal on the specified query nodes
% (gibbs_sampling_engine)
% [marginal, engine] = marginal_nodes(engine, nodes, ...)
%
% returns Pr(X(nodes) | X(observedNodes))
%
% The engine is also modified, and so it is returned as well, since
% Matlab doesn't support passing by reference(!) So
% if you want to, for example, incrementally run gibbs for a few 100
% steps at a time, you should use the returned value.
%
% Optional arguments :
%
% 'reset_counts' is 1 if you want to reset the counts made in the
% past, and 0 otherwise (if the current query nodes are different
% from the previous query nodes, or if marginal_nodes has not been
% called before, reset_counts should be set to 1).
% By default it is 1.
reset_counts = 1;
if (nargin > 3)
args = varargin;
nargs = length(args);
for i = 1:2:nargs
switch args{i}
case 'reset_counts'
reset_counts = args{i+1};
otherwise
error(['Incorrect argument to gibbs_sampling_engine/' ...
' marginal_nodes']);
end
end
end
% initialization stuff
bnet = bnet_from_engine(engine);
slice_size = engine.slice_size;
hnodes = engine.hnodes;
onodes = engine.onodes;
nonqnodes = mysetdiff(1:slice_size, nodes);
gap = engine.gap;
burnin = engine.burnin;
T_max = engine.T;
ns = bnet.node_sizes(nodes);
% Cache the strides for the marginal table
marg_strides = [1 cumprod(ns(1:end-1))];
% Reset counts if necessary
if (reset_counts == 1)
%state = sample_bnet(bnet, 1, 0);
%state = cell2num(sample_bnet(bnet, 'evidence', num2cell(engine.evidence)));
state = cell2num(sample_bnet(bnet));
state(onodes) = engine.evidence(onodes);
if (length(ns) == 1)
marginal_counts = zeros(ns(1),1);
else
marginal_counts = zeros(ns);
end
% Otherwise, use the counts that have been stored in the engine
else
state = engine.state;
state(onodes, :) = engine.evidence(onodes, :);
marginal_counts = engine.marginal_counts;
end
if (engine.deterministic == 1)
pos = 1;
order = engine.order;
orderSize = length(engine.order);
else
sampling_dist = normalise(engine.sampling_dist);
end
for t = 1:(T_max*gap+burnin)
% First, select node m to sample
if (engine.deterministic == 1)
m = engine.order(pos);
pos = pos+1;
if (pos > orderSize)
pos = 1;
end
else
m = my_sample_discrete(sampling_dist);
end
% If the node is observed, then don't bother resampling
if (myismember(m, onodes))
continue;
end
% Next, compute the posterior
post = compute_posterior (bnet, state, m, engine.strides, engine.families, ...
engine.children, engine.CPT);
state(m) = my_sample_discrete(post);
% Now update our monte carlo estimate of the posterior
% distribution on the query node
if ((mod(t-burnin, gap) == 0) & (t > burnin))
vals = state(nodes);
index = 1+marg_strides*(vals-1);
marginal_counts(index) = marginal_counts(index)+1;
end
end
% Store results for future computation. Note that we store
% unnormalized counts
engine.state = state;
engine.marginal_counts = marginal_counts;
marginal.T = normalise(marginal_counts);
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