1 | clear all |
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2 | % name random variables |
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3 | x = RV({'x'},1); |
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4 | y = RV({'y'},1); |
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5 | u = RV({'u'},1); |
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6 | |
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7 | % create f(x_t| x_{t-1}, u_{t}) |
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8 | fx.class = 'mlnorm<ldmat>'; |
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9 | fx.rv = x; |
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10 | fx.rvc = RVtimes([x,u], [-1, 0]); |
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11 | fx.A = [0.5 -0.9]; |
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12 | fx.const = 0; |
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13 | fx.R = 0.01; |
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14 | |
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15 | % create f(y_t| y_{t-1}, u_{t-1}) |
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16 | fy.class = 'mlnorm<ldmat>'; |
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17 | fy.rv = y; |
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18 | fy.rvc = RVjoin([x,u]); |
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19 | fy.A = [1, 0.1]; |
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20 | fy.const = 0; |
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21 | fy.R = 1e-3; |
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22 | |
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23 | % create f(u_t| ) |
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24 | fu.class = 'egauss'; |
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25 | fu.rv = u; |
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26 | fu.mu = 0; |
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27 | fu.R = 1e-1; |
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28 | |
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29 | % create DS |
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30 | DS.class = 'PdfDS'; |
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31 | DS.pdf.class = 'mprod'; |
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32 | DS.pdf.pdfs = { fy, fx, fu}; |
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33 | DS.init_rv = RVtimes([x], [-1]); |
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34 | DS.init_values = [0.1]; |
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35 | |
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36 | % debug DS % MMM=simulator(DS); |
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37 | |
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38 | %%%%%% PF estimator |
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39 | |
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40 | PF.class = 'PF'; |
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41 | PF.particle.class = 'BootstrapParticle'; |
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42 | PF.particle.parameter_pdf = fx; |
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43 | PF.particle.observation_pdf = fy; |
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44 | PF.log_level ='logbounds,logevidence'; |
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45 | PF.prior.class = 'egauss'; |
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46 | PF.prior.mu = 0; |
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47 | PF.prior.R = 0.2; |
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48 | PF.res_threshold = 1; |
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49 | PF.n = 1000; |
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50 | |
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51 | exper.ndat =100; |
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52 | |
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53 | M=estimator(DS,{PF},exper); |
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54 | |
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55 | %%%%%%%%%%%%%%%%%%%%%%%%%%%%%% |
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56 | % plot results |
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57 | ndat = size(M.DS_dt_u,1); |
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58 | |
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59 | plotestimates(M.DS_dt_x, ... |
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60 | M.Est0_apost_mean_x, ... |
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61 | M.Est0_apost_lbound_x, ... |
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62 | M.Est0_apost_ubound_x); |
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63 | set(gca,'YLim',[-1.5,1]); |
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64 | |
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65 | title('Variance parameters r') |
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