Histogram Binwidth Optimization 1.0

function [optN, C, N] = sshist(x,N) [optN, C, N] = sshist(x,N) Function `sshist' returns the optimal number of bins in a histogram used for density estimation. Optimization principle is to minimize expected L2 loss function between the histogram and an unknown underlying density function. An assumption made is merely that samples are drawn from the density independently each other. The optimal binwidth D* is obtained as a minimizer of the formula, (2K-V) / D^2, where K and V are mean and variance of sample counts across bins with width D. Optimal number of bins is given as (max(x) - min(x)) / D*. For more information, visit http://2000.jukuin.keio.ac.jp/shimazaki/res/histogram.html Original paper: Hideaki Shimazaki and Shigeru Shinomoto A method for selecting the bin size of a time histogram Neural Computation 19(6), 1503-1527, 2007 http://dx.doi.org/10.1162/neco.2007.19.6.1503 Example usage: optN = sshist(x); hist(x,optN); Input argument x: Sample data vector. N (optinal):A vector that specifies the number of bins to be examined. The optimal number of bins is selected from the elements of N. Default value is N = 2:50. * Do not search binwidths smaller than a sampling resolution of data.

Requirements:	No special requirements
Platforms:	Matlab
Keyword:	250 Do,  2kv D2,  3d 250,  And An,  Are Mean,  Assumption,  Assumption Made,  Between The,  Bin Size,  Bins,  Drawn,  Examined,  Maxx,  Optimal Number,  Shigeru,  Sshistx,  Sshistxn,  Unknown,  Variance
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