Mauch MIREX 2010

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