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diff --git a/sourcecodes/bnt-master/BNT/inference/static/@belprop_inf_engine/find_mpe.m b/sourcecodes/bnt-master/BNT/inference/static/@belprop_inf_engine/find_mpe.m
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+function mpe = find_mpe(engine, evidence, varargin)
+% FIND_MPE Find the most probable explanation of the data  (belprop)
+% function mpe = find_mpe(engine, evidence,...)
+%
+% evidence{i} = [] if X(i) is hidden, and otherwise contains its observed value (scalar or column vector).
+%
+% This finds the marginally most likely value for each hidden node,
+% and may give the wrong results even if the graph is acyclic,
+% unless you set break_ties = 1.
+%
+% The following optional arguments can be specified in the form of name/value pairs:
+% [default value in brackets]
+%
+% break_ties is optional. If 1, we will force ties to be broken consistently
+%  by calling enter_evidence N times. (see Jensen96, p106) Default = 1.
+
+break_ties = 1;
+
+% parse optional params
+args = varargin;
+nargs = length(args);
+for i=1:2:nargs
+  switch args{i},
+   case 'break_ties',    break_ties = args{i+1}; 
+   otherwise,  
+    error(['invalid argument name ' args{i}]);       
+  end
+end
+
+engine = enter_evidence(engine, evidence, 'maximize', 1);
+
+observed = ~isemptycell(evidence);
+evidence = evidence(:); % hack to handle unrolled DBNs
+N = length(evidence);
+mpe = cell(1,N);
+for i=1:N
+  m = marginal_nodes(engine, i);
+  % observed nodes are all set to 1 inside the inference engine, so we must undo this
+  if observed(i)
+    mpe{i} = evidence{i};
+  else
+    mpe{i} = argmax(m.T);
+    if break_ties
+      evidence{i} = mpe{i};                             
+      [engine, ll] = enter_evidence(engine, evidence, 'maximize', 1);  
+    end
+  end
+end
+