% PURPOSE : To estimate the states of the following nonlinear, %           nonstationary state space model:%           x(t+1) = 0.5x(t) + [25x(t)]/[(1+x(t))^(2)] %                    + 8cos(1.2t) + process noise%           y(t) =  x(t)^(2) / 20  + measurement noise%           using the sequential importance-samping resampling%           algorithm.                                        % AUTHOR  : Nando de Freitas - Thanks for the acknowledgement :-)% DATE    : 08-09-98clear;echo off;% INITIALISATION AND PARAMETERS:% =============================N = 50;                % Number of time steps.t = 1:1:N;             % Time.  x0 = 0.1;              % Initial state.x = zeros(N,1);        % Hidden states.y = zeros(N,1);        % Observations.x(1,1) = x0;           % Initial state. R = 1;                 % Measurement noise variance.Q = 10;                % Process noise variance.      initVar = 5;           % Initial variance of the states.  numSamples=500;        % Number of Monte Carlo samples per time step. % GENERATE PROCESS AND MEASUREMENT NOISE:% ======================================v = randn(N,1);w = sqrt(10)*randn(N,1);figure(1)clf;subplot(221)plot(v);ylabel('Measurement Noise','fontsize',15);xlabel('Time');subplot(222)plot(w);ylabel('Process Noise','fontsize',15);xlabel('Time');% GENERATE STATE AND MEASUREMENTS:% ===============================y(1,1) = (x(1,1)^(2))./20 + v(1,1); for t=2:N,  x(t,1) = 0.5*x(t-1,1) + 25*x(t-1,1)/(1+x(t-1,1)^(2)) + 8*cos(1.2*(t-1)) + w(t,1);  y(t,1) = (x(t,1).^(2))./20 + v(t,1); end;figure(1)subplot(223)plot(x)ylabel('State x','fontsize',15);xlabel('Time','fontsize',15);subplot(224)plot(y)ylabel('Observation y','fontsize',15);xlabel('Time','fontsize',15);fprintf('Press a key to continue')  pause;fprintf('\n')fprintf('Simulation in progress')fprintf('\n')% PERFORM SEQUENTIAL MONTE CARLO FILTERING:% ========================================[samples,q] = bootstrap(x,y,R,Q,initVar,numSamples);% COMPUTE CENTROID, MAP AND VARIANCE ESTIMATES:% ============================================% Posterior mean estimateprediction = mean(samples);% Posterior peak estimatebins = 20;xmap=zeros(N,1);for t=1:N  [p,pos]=hist(samples(:,t,1),bins);  map=find(p==max(p));  xmap(t,1)=pos(map(1));end;% Posterior standard deviation estimatexstd=std(samples);% PLOT RESULTS:% ============figure(1)clf;subplot(221)plot(1:length(x),x,'g-*',1:length(x),prediction,'r-+',1:length(x),xmap,'m-*')legend('True value','Posterior mean estimate','MAP estimate');ylabel('State estimate','fontsize',15)xlabel('Time','fontsize',15)subplot(222)plot(prediction,x,'+')ylabel('True state','fontsize',15)xlabel('Posterior mean estimate','fontsize',15)hold onc=-25:1:25;plot(c,c,'r');axis([-25 25 -25 25]);hold offsubplot(223)plot(xmap,x,'+')ylabel('True state','fontsize',15)xlabel('Posterior mean estimate','fontsize',15)hold onc=-25:1:25;plot(c,c,'r');axis([-25 25 -25 25]);hold off% Plot histogram:[rows,c]=size(q);domain = zeros(rows,1);range = zeros(rows,1);parameter = 1;bins = 10;support=[-20:1:20];figure(1)subplot(224)hold onylabel('Time','fontsize',15)xlabel('Sample space','fontsize',15)zlabel('Posterior density','fontsize',15)v=[0 1];caxis(v);for t=1:2:N,  [range,domain]=hist(samples(:,t,parameter),support);  waterfall((domain),t,range)end;