Difference between revisions of "RegressionNoise"
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| − | [[Category: | + | [[Category: Distribution Functions]] |
| − | [[Category:Statistical Functions]] | + | [[Category: Statistical Functions]] |
| − | [[Category:Doc Status D]] | + | [[Category: Multivariate Distributions library functions]] |
| + | [[Category: Doc Status D]] | ||
| − | = RegressionNoise(Y,B,I,K,C) = | + | == RegressionNoise(Y, B, I, K, ''C'') == |
| − | When you have data, | + | When you have data, «Y[I]» and «B[I, K]», generated from an underlying model with unknown coefficients «C[K]» and «S» of the form: |
| − | + | :<code>Y = Sum(C*B, I) + Normal(0, S)</code> | |
| − | This function computes an estimate for | + | This function computes an estimate for «S» by assuming that the sample noise is the same for each point in the data set. |
| − | When using in conjunction with [[RegressionDist]], it is most efficient to provide the optional parameter | + | When using in conjunction with [[RegressionDist]], it is most efficient to provide the optional parameter «C» to both routines, where «C» is the expected value of the regression coefficients, obtained from calling [[Regression]](Y, B, I, K). Doing so avoids an unnecessary call to the builtin [[Regression]] function. |
| − | = Library = | + | == Library == |
| + | Multivariate Distributions library functions ([[media:Multivariate Distributions.ana |Multivariate Distributions.ana]]) | ||
| + | :Use '''File → Add Library...''' to add this library | ||
| − | + | == See Also == | |
| − | |||
| − | = See Also = | ||
* [[RegressionDist]] | * [[RegressionDist]] | ||
* [[RegressionFitProb]] | * [[RegressionFitProb]] | ||
* [[Regression]] | * [[Regression]] | ||
| + | * [[Multivariate distributions]] | ||
Latest revision as of 00:37, 24 February 2016
RegressionNoise(Y, B, I, K, C)
When you have data, «Y[I]» and «B[I, K]», generated from an underlying model with unknown coefficients «C[K]» and «S» of the form:
Y = Sum(C*B, I) + Normal(0, S)
This function computes an estimate for «S» by assuming that the sample noise is the same for each point in the data set.
When using in conjunction with RegressionDist, it is most efficient to provide the optional parameter «C» to both routines, where «C» is the expected value of the regression coefficients, obtained from calling Regression(Y, B, I, K). Doing so avoids an unnecessary call to the builtin Regression function.
Library
Multivariate Distributions library functions (Multivariate Distributions.ana)
- Use File → Add Library... to add this library
See Also
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