动态贝叶斯网络算法的计算和改进_FullFlexBayesNets_matlab源码

% This case study uses Entropy Pooling to compute Fully Flexible Bayesian networks for risk management, see % A. Meucci (2010) "Fully Flexible Bayesian Networks", working paper % % Most recent version of article and code available at % http://www.symmys.com/node/152 clear; clc; close all; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % upload scenarios table and prior distribution for changes in % SWAP2YR SWAP10YR CDXIG S&P500 DollarIndex Crude Gold VIX 10YRInflationSwapRate load FreaqEst [J,N]=size(X); e=.01; p=(1-e)*p+e*ones(J,1)/J; [m,s,C]=ComputeMoments(X,p); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % input views % statement: View(k).Who (e.g. [1 3])= View(k).Equal (e.g. {[2 3] [1 3 5]}) % optional conditional statement: View(k).Cond_Who (e.g. [2])= View(k).Cond_Equal (e.g. {[1]}) % amount of stress is quantified as Prob(statement) <= View(k).v if View(k).sgn = 1; % Prob(statement) >= View(k).v if View(k).sgn = -1; % confidence in stress is quantified in View(k).c in (0,1) for n=1:N k=2*n-1; View(k).Who=[n]; View(k).Equal={[-1]}; View(k).Cond_Who=[]; View(k).Cond_Equal={[]}; View(k).v = .4; View(k).sgn =-1; View(k).c=.5; k=2*n; View(k).Who=[n]; View(k).Equal={[1]}; View(k).Cond_Who=[]; View(k).Cond_Equal={[]}; View(k).v = .4; View(k).sgn =-1; View(k).c=.5; end k=2*N+1; View(k).Who=[1 2 8]; View(k).Equal={[-1 0] [-1 0] [1]}; View(k).Cond_Who=[4]; View(k).Cond_Equal={[-1]}; View(k).v = .9; View(k).sgn =-1; View(k).c=.1; % create linear constraint representation of views on probabilities [A,b,g] = CondProbViews(View,X); % add constraint for view on correlation C_12_=.6; New_A=(X(:,1).*X(:,2))'; New_b=s(1)*s(2)*C_12_ + m(1)*m(2); New_g=-log(1-.1); A=[A New_A]; b=[b New_b]; g=[g New_g]; % enforce consistency db = Tweak(A,b,g); b = b+db; %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % compute posterior Aeq = ones(1,J); % constrain probabilities to sum to one beq = 1; p_ = EntropyProg(p,A,b,Aeq ,beq); %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % plots figure subplot(2,1,1) bar(p) YLim=get(gca,'ylim'); subplot(2,1,2) bar(p_) set(gca,'ylim',YLim);