shift_in_mean_and_variance: find best split shift in mean and variance
In mlondschien/hdcd: High - Dimensional Change Point Detection
Description
Usage
Arguments
Value
Description
Efficiently calculates the gain in a one-dimensional shift in mean and variance scenario.
Usage
1
Arguments
x
Array with entries that are assumed to have a shift in mean and variance at some split point.
alpha
array of segment boundaries
train_fold
array containing indices in training fold
Value
An array on length length(x) with gains resulting from splitting at that specific split point.
The negative gaussian loglikelihood of observations x for estimated mean and variance is -n/2 * (log(2 π \hatσ^2) + 1).
The gaussian maximum likelihood estimate \hatσ^2 is (sum(x^2) - sum(x)^2/length(x))/length(x)
mlondschien/hdcd documentation built on Jan. 5, 2021, 11:26 p.m.
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Description Usage Arguments Value
Description
Efficiently calculates the gain in a one-dimensional shift in mean and variance scenario.
Usage
1 |
Arguments
x |
Array with entries that are assumed to have a shift in mean and variance at some split point. |
alpha |
array of segment boundaries |
train_fold |
array containing indices in training fold |
Value
An array on length length(x) with gains resulting from splitting at that specific split point.
The negative gaussian loglikelihood of observations x for estimated mean and variance is -n/2 * (log(2 π \hatσ^2) + 1).
The gaussian maximum likelihood estimate \hatσ^2 is (sum(x^2) - sum(x)^2/length(x))/length(x)
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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