marginal_node.m

来自「Bayes网络工具箱」· M 代码 · 共 45 行

function marginal = marginal_node(engine, i, t)
% MARGINAL_NODE Compute the marginal on the specified node (bk)
%
% marginal = marginal_node(engine, i, t)
% returns Pr(X(i,t) | Y(1:T)), where X(i,t) is the i'th node in the t'th slice.
% If enter_evidence used filtering instead of smoothing, this will return  Pr(X(i,t) | Y(1:t)).

if nargin < 3, t = 1; end

% clpot{t} contains slice t-1 and t
% Example
% clpot #: 1    2    3
% slices:  1  1,2  2,3
% For filtering, we must take care not to take future evidence into account.
% Hence, we use clpot{1} if t=1.
% For smoothing, clpot{1} does not exist.

bnet = bnet_from_engine(engine);
ss = length(bnet.intra);

if t == 1
  nodes = i;
  if engine.filter
    slice = 1;
    c = clq_containing_family(engine.sub_engine1, i);
  else
    slice = 2;
    c = clq_containing_family(engine.sub_engine, i);
  end
else
  nodes = i+ss;
  slice = t;
  c = clq_containing_family(engine.sub_engine, i+ss);
end
assert(c >= 1);
clpot = engine.clpot{slice};
bigpot = clpot{c};

marginal = pot_to_marginal(marginalize_pot(bigpot, nodes));

% we convert the domain to the unrolled numbering system
% so that update_ess extracts the right evidence.
marginal.domain = nodes+(t-1)*ss;