Slide: Another Illustration of Degeneracy for Particle Learning pdf , n=1000  04 .0 03 .0 02 .0 01 .0 0.8 0 0 .9 1 1 .1 1 .2 04 .0 03 .0 02 .0 01 .0 0.8 0 0 .9 1 1 .1 1 .2 04 .0 03 .0 02 .0 01 .0 0.8 0 0 .9 1 1 .1 1 .2 04 .0 03 .0 02 .0 01 .0 0.8 0 0 .9 1 1 .1 1 .2 04 .0 03 .0 02 .0 01 .0 0.8 0 0 .9 1 2 y 1 .1 1 .2  0 .1 05 .0 0.4 0 06 .0 04 .0 02 .0 0.4 0 0 .1 05 .0 0.4 0 0 .1 05 .0 0.4 0 0 .1 05 .0 0.4 0 0 .5 0 .6 0 .7 0 .8 0 .9 0 .5 0 .6 0 .7 0 .8 0 .9 0 .5 0 .6 0 .7 0 .8 0 .9 0 .5 0 .6 0 .7 0 .8 0 .9 0 .5 0 .6 0 .7 0 .8 0 .9  pdf , n=5000  pdf , n=4000  pdf , n=3000  pdf , n=2000    Figure: Estimates of p ( j y1 :t ) and p 2 y1 :t over 50 runs (red) vs ground truth (blue) for t = 103 , 2.103 , ..., 5.103 for N = 104 . A. Doucet (MLSS Sept. 2012) Sept. 2012 110 / 136
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