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author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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wolffd@0 | 1 <html> |
wolffd@0 | 2 <head> |
wolffd@0 | 3 <title> |
wolffd@0 | 4 Netlab Reference Manual gauss |
wolffd@0 | 5 </title> |
wolffd@0 | 6 </head> |
wolffd@0 | 7 <body> |
wolffd@0 | 8 <H1> gauss |
wolffd@0 | 9 </H1> |
wolffd@0 | 10 <h2> |
wolffd@0 | 11 Purpose |
wolffd@0 | 12 </h2> |
wolffd@0 | 13 Evaluate a Gaussian distribution. |
wolffd@0 | 14 |
wolffd@0 | 15 <p><h2> |
wolffd@0 | 16 Synopsis |
wolffd@0 | 17 </h2> |
wolffd@0 | 18 <PRE> |
wolffd@0 | 19 y = gauss(mu, covar, x) |
wolffd@0 | 20 </PRE> |
wolffd@0 | 21 |
wolffd@0 | 22 |
wolffd@0 | 23 <p><h2> |
wolffd@0 | 24 Description |
wolffd@0 | 25 </h2> |
wolffd@0 | 26 |
wolffd@0 | 27 <p><CODE>y = gauss(mu, covar, x)</CODE> evaluates a multi-variate Gaussian |
wolffd@0 | 28 density in <CODE>d</CODE>-dimensions at a set of points given by the rows |
wolffd@0 | 29 of the matrix <CODE>x</CODE>. The Gaussian density has mean vector <CODE>mu</CODE> |
wolffd@0 | 30 and covariance matrix <CODE>covar</CODE>. |
wolffd@0 | 31 |
wolffd@0 | 32 <p><h2> |
wolffd@0 | 33 See Also |
wolffd@0 | 34 </h2> |
wolffd@0 | 35 <CODE><a href="gsamp.htm">gsamp</a></CODE>, <CODE><a href="demgauss.htm">demgauss</a></CODE><hr> |
wolffd@0 | 36 <b>Pages:</b> |
wolffd@0 | 37 <a href="index.htm">Index</a> |
wolffd@0 | 38 <hr> |
wolffd@0 | 39 <p>Copyright (c) Ian T Nabney (1996-9) |
wolffd@0 | 40 |
wolffd@0 | 41 |
wolffd@0 | 42 </body> |
wolffd@0 | 43 </html> |