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 contextns = bnet.node_sizes(:);CPD.Qsizes = ns(Qnodes);CPD.d = d;CPD.D = D;Qsz = ns(CPD.Q);Qpsz = prod(ns(CPD.Qps));% set default argumentsp = 0.9;%termprob(k,i,t) Might terminate if i=Qsz; will not terminate if i<Qsztermprob = 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}])  endendps = [CPD.Qps CPD.Q];% ns(self) = 2 since this is an F nodeCPD.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);