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The choice of optimum bandwidth (\textit {h} value) is important. However, even with the optimum bandwidth, one still needs enough number of points for successful kernel density estimation. In this figure the best $h$ value is $1$. In terms of sample size, the value of $50$ appears to be too small. As we increase the sample size to $100$, we get better fit between estimated and actual probability values. But for a very close fit, we need to go up to $1000$ sample points.}}{25}} \@writefile{lof}{\contentsline {subfigure}{\numberline{(a)}{\ignorespaces {50 Sample Points: Note the bad fit due to low sample size.}}}{25}} \@writefile{lof}{\contentsline {subfigure}{\numberline{(b)}{\ignorespaces {100 Sample Points: Note the better fit due to increased sample size.}}}{25}} \@writefile{lof}{\contentsline {subfigure}{\numberline{(c)}{\ignorespaces {1000 Sample Points: Note the optimum fit due to high sample size.}}}{25}} \newlabel{fig:sample-size-kernel-estimation}{{13}{25}}