Mauch MIREX 2010

function CPD = learn_params(CPD, fam, data, ns, cnodes)

%function CPD = learn_params(CPD, fam, data, ns, cnodes)

% LEARN_PARAMS Compute the maximum likelihood estimate of the params of a gaussian CPD given complete data

% CPD = learn_params(CPD, fam, data, ns, cnodes)

%

% data(i,m) is the value of node i in case m (can be cell array).

% We assume this node has a maximize_params method.

ncases = size(data, 2);

CPD = reset_ess(CPD);

% make a fully observed joint distribution over the family

fmarginal.domain = fam;

fmarginal.T = 1;

fmarginal.mu = [];

fmarginal.Sigma = [];

if ~iscell(data)

  cases = num2cell(data);

else

  cases = data;

end

hidden_bitv = zeros(1, max(fam));

for m=1:ncases

  % specify (as a bit vector) which elements in the family domain are hidden

  hidden_bitv = zeros(1, max(fmarginal.domain));

  ev = cases(:,m);

  hidden_bitv(find(isempty(ev)))=1;

  CPD = update_ess(CPD, fmarginal, ev, ns, cnodes, hidden_bitv);

end

CPD = maximize_params(CPD);