Fig. 4From: A weighted likelihood criteria for learning importance densities in particle filteringUnivariate frequency histograms of xn,1 (top row) and xn,2 (bottom row) in Example 1 in the case of filtering at n=T. The non-normality (asymmetries) of the frequency histograms is well approximated by the fitted GMM (solid line). The histograms are constructed using resamples from \(\left \{x^{i}_{T},w^{i}_{T}\right \}^{M}_{i=1}\)Back to article page