function CPD = hhmmF_CPD(bnet, self, Qself, Fbelow, varargin)% HHMMF_CPD Make the CPD for an F node in a hierarchical HMM% CPD = hhmmF_CPD(bnet, self, Qself,  Fbelow, ...)%%        Qps%          \%           \%           Fself%         /   |%        /    |%       Qself Fbelow%% We assume nodes are ordered (numbered) as follows: Qps, Q, Fbelow, F% All nodes numbers should be from slice 1.%% If Fbelow if missing, this becomes a regular tabular_CPD.% Qps may be omitted.%% optional args [defaults]% % Qps - node numbers.% termprob - termprob(k,i,2) = prob finishing given Q(d)=i and Q(1:d-1)=k [ finish in last state wp 0.9]%% 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.Qps = [];% get parentsfor i=1:2:length(varargin)  switch varargin{i},   case 'Qps', Qps = varargin{i+1};   endendns = bnet.node_sizes(:);Qsz = ns(Qself);Qpsz = prod(ns(Qps));CPD.Qsz = Qsz;CPD.Qpsz = Qpsz;ps = parents(bnet.dag, self);CPD.Fbelow_ndx = find_equiv_posns(Fbelow, ps);CPD.Qps_ndx = find_equiv_posns(Qps, ps);CPD.Qself_ndx = find_equiv_posns(Qself, ps);% 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};   endendCPD.sub_CPD_term = mk_isolated_tabular_CPD([Qpsz Qsz 2], {'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);