Mauch MIREX 2010

function CPD = hhmmF_CPD(bnet, self, Qnodes, d, D, varargin)

% HHMMF_CPD Make the CPD for an F node at depth D of a D-level hierarchical HMM

% CPD = hhmmF_CPD(bnet, self, Qnodes, d, D, ...)

%

%    Q(d-1)

%          \

%           \

%           F(d)

%         /   |

%        /    |

%    Q(d)  F(d+1)

%

% We assume nodes are ordered (numbered) as follows:

% Q(1), ... Q(d), F(d+1), F(d)

%

% F(d)=2 means level d has finished. The prob this happens depends on Q(d)

% and optionally on Q(d-1), Q(d=1), ..., Q(1).

% Also, level d can only finish if the level below has finished

% (hence the F(d+1) -> F(d) arc).

%

% If d=D, there is no F(d+1), so F(d) is just a regular tabular_CPD.

% If all models always finish in the same state (e.g., their last),

% we don't need to condition on the state of parent models (Q(d-1), ...)

%

% optional args [defaults]

%

% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state ]

%

% hhmmF_CPD is a subclass of tabular_CPD so we inherit inference methods like CPD_to_pot, etc.

%

% We create an isolated tabular_CPD with no F parent to learn termprob

% so we can avail of e.g., entropic or Dirichlet priors.

%

% For details, see "Linear-time inference in hierarchical HMMs", Murphy and Paskin, NIPS'01.

ps = parents(bnet.dag, self);

Qps = myintersect(ps, Qnodes);

F = mysetdiff(ps, Qps);

CPD.Q = Qps(end); % Q(d)

assert(CPD.Q == Qnodes(d));

CPD.Qps = Qps(1:end-1); % all Q parents except Q(d), i.e., calling context

ns = bnet.node_sizes(:);

CPD.Qsizes = ns(Qnodes);

CPD.d = d;

CPD.D = D;

Qsz = ns(CPD.Q);

Qpsz = prod(ns(CPD.Qps));

% set default arguments

p = 0.9;

%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsz

termprob = zeros(Qpsz, Qsz, 2);

termprob(:, Qsz, 2) = p; 

termprob(:, Qsz, 1) = 1-p; 

termprob(:, 1:(Qsz-1), 1) = 1; 

for i=1:2:length(varargin)

  switch varargin{i},

   case 'termprob', termprob = varargin{i+1}; 

   otherwise, error(['unrecognized argument ' varargin{i}])

  end

end

ps = [CPD.Qps CPD.Q];

% ns(self) = 2 since this is an F node

CPD.sub_CPD_term = mk_isolated_tabular_CPD(ps, ns([ps self]), {'CPT', termprob});

S = struct(CPD.sub_CPD_term);

CPD.termprob = S.CPT;

CPD = class(CPD, 'hhmmF_CPD', tabular_CPD(bnet, self));

CPD = update_CPT(CPD);