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Cumulative Mean and Variance

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>> Y = cummean(X,DIM);
if X is MxN, Y is also MxN. To illustrate the functionality, lets assume X is a 1xN vector. Then, Y is a 1xN vector where the n-th entry in Y is given by mean(X(1:n)). So, Y(end) = mean(X), and Y(1) = X(1). cumvar works in the same way, and this can be done on an arbitrary dimensional X along dimension DIM. See screenshot for an example.

These functions are useful (at least to me) for determining how many iterations of a process are required until the mean and variance of the process are stable (ie: not changing with increasing iterations). Maybe useful to people doing Markhov chain stuff?

Free download from Shareware Connection - These functions are useful (at least to me) for determining how many iterations of a process are required until the mean and variance of the process are stable (ie: not changing with increasing iterations).

Publisher: Sumedh Joshi | License: Freeware | Price: 0.00
Version: 1.0 | Size: 10 KB | Platform: Matlab, Scripts
Released Date: 17-05-2013 | Rating: 0 | Title: Cumulative Mean and Variance

Author Url: http://www.mathworks.com
Program Info Url: http://www.mathworks.com
Download Url: http://www.mathworks.com/matlabcentral/fx_files/26791/1/cumstats.zip

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