Mauch MIREX 2010

function CPD = bayes_update_params(CPD, self_ev, pev)

% UPDATE_PARAMS_COMPLETE Bayesian parameter updating given completely observed data (tabular)

% CPD = update_params_complete(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 can be arrays or cell arrays.

%

% We update the Dirichlet pseudo counts and set the CPT to the mean of the posterior.

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

ncases = length(self_ev);

sz = CPD.sizes;

nparents = length(sz)-1;

assert(nparents == size(pev,1));

if ncases == 0 | ~adjustable_CPD(CPD)

  return;

elseif ncases == 1 % speedup the sequential learning case by avoiding normalization of the whole array

  if usecell

    x = cat(1, pev{:})';

    y = self_ev{1};

  else

    x = pev(:)';

    y = self_ev;

  end

  switch nparents

   case 0,

    CPD.dirichlet(y) = CPD.dirichlet(y)+1;

    CPD.CPT = CPD.dirichlet / sum(CPD.dirichlet);

   case 1,

    CPD.dirichlet(x(1), y) = CPD.dirichlet(x(1), y)+1;

    CPD.CPT(x(1), :) = CPD.dirichlet(x(1), :) ./ sum(CPD.dirichlet(x(1), :));

   case 2,

    CPD.dirichlet(x(1), x(2), y) = CPD.dirichlet(x(1), x(2), y)+1;

    CPD.CPT(x(1), x(2), :) = CPD.dirichlet(x(1), x(2), :) ./ sum(CPD.dirichlet(x(1), x(2), :));

   case 3,

    CPD.dirichlet(x(1), x(2), x(3), y) = CPD.dirichlet(x(1), x(2), x(3), y)+1;

    CPD.CPT(x(1), x(2), x(3), :) = CPD.dirichlet(x(1), x(2), x(3), :) ./ sum(CPD.dirichlet(x(1), x(2), x(3), :));

   otherwise,

    ind = subv2ind(sz, [x y]);

    CPD.dirichlet(ind) = CPD.dirichlet(ind) + 1;

    CPD.CPT = mk_stochastic(CPD.dirichlet);

  end

else  

  if usecell

    data = [cell2num(pev); cell2num(self_ev)]; 

  else

    data = [pev; self_ev];

  end

  counts = compute_counts(data, sz);

  CPD.dirichlet = CPD.dirichlet + counts;

  CPD.CPT = mk_stochastic(CPD.dirichlet);

end