Mauch MIREX 2010

function L = log_prob_node(CPD, self_ev, pev)

% LOG_PROB_NODE Compute prod_m log P(x(i,m)| x(pi_i,m), theta_i) for node i (gaussian)

% L = log_prob_node(CPD, self_ev, pev)

%

% 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.)

if iscell(self_ev), usecell = 1; else usecell = 0; end

use_log = 1;

ncases = length(self_ev);

nparents = length(CPD.sizes)-1;

assert(ncases == size(pev, 2));

if ncases == 0

  L = 0;

  return;

end

if length(CPD.dps)==0 % no discrete parents, so we can vectorize

  i = 1;

  if usecell

    Y = cell2num(self_ev);

  else

    Y = self_ev;

  end

  if length(CPD.cps) == 0 

    L = gaussian_prob(Y, CPD.mean(:,i), CPD.cov(:,:,i), use_log);

  else

    if usecell

      X = cell2num(pev);

    else

      X = pev;

    end

    L = gaussian_prob(Y, CPD.mean(:,i) + CPD.weights(:,:,i)*X, CPD.cov(:,:,i), use_log);

  end

else % each case uses a (potentially) different set of parameters

  L = 0;

  for m=1:ncases

    if usecell

      dpvals = cat(1, pev{CPD.dps, m});

    else

      dpvals = pev(CPD.dps, m);

    end

    i = subv2ind(CPD.sizes(CPD.dps), dpvals(:)');

    y = self_ev{m};

    if length(CPD.cps) == 0 

      L = L + gaussian_prob(y, CPD.mean(:,i), CPD.cov(:,:,i), use_log);

    else

      if usecell

	x = cat(1, pev{CPD.cps, m});

      else

	x = pev(CPD.cps, m);

      end

      L = L + gaussian_prob(y, CPD.mean(:,i) + CPD.weights(:,:,i)*x, CPD.cov(:,:,i), use_log);

    end

  end

end