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| Statistics.KernelDensity | | Portability | portable | | Stability | experimental | | Maintainer | bos@serpentine.com |
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| Description |
| Kernel density estimation code, providing non-parametric ways to
estimate the probability density function of a sample.
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| Synopsis |
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| Simple entry points
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| :: Int | Number of points at which to estimate
| | -> Sample | | | -> (Points, Vector Double) | | | Simple Epanechnikov kernel density estimator. Returns the
uniformly spaced points from the sample range at which the density
function was estimated, and the estimates at those points.
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| :: Int | Number of points at which to estimate
| | -> Sample | | | -> (Points, Vector Double) | | | Simple Gaussian kernel density estimator. Returns the uniformly
spaced points from the sample range at which the density function
was estimated, and the estimates at those points.
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| Building blocks
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| Choosing points from a sample
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| Points from the range of a Sample.
| | Constructors | |
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| :: Int | Number of points to select, n
| | -> Double | Sample bandwidth, h
| | -> Sample | Input data
| | -> Points | | Choose a uniform range of points at which to estimate a sample's
probability density function.
If you are using a Gaussian kernel, multiply the sample's bandwidth
by 3 before passing it to this function.
If this function is passed an empty vector, it returns values of
positive and negative infinity.
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| Bandwidth estimation
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| The width of the convolution kernel used.
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| Compute the optimal bandwidth from the observed data for the given
kernel.
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| Bandwidth estimator for an Epanechnikov kernel.
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| Bandwidth estimator for a Gaussian kernel.
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| Kernels
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The convolution kernel. Its parameters are as follows:
- Scaling factor, 1/nh
- Bandwidth, h
- A point at which to sample the input, p
- One sample value, v
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| Epanechnikov kernel for probability density function estimation.
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| Gaussian kernel for probability density function estimation.
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| Low-level estimation
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| :: Kernel | Kernel function
| | -> Bandwidth | Bandwidth, h
| | -> Sample | Sample data
| | -> Points | Points at which to estimate
| | -> Vector Double | | | Kernel density estimator, providing a non-parametric way of
estimating the PDF of a random variable.
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| :: Double -> Double | Bandwidth function
| | -> Kernel | Kernel function
| | -> Double | Bandwidth scaling factor (3 for a Gaussian kernel, 1 for all others)
| | -> Int | Number of points at which to estimate
| | -> Sample | Sample data
| | -> (Points, Vector Double) | | | A helper for creating a simple kernel density estimation function
with automatically chosen bandwidth and estimation points.
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| Produced by Haddock version 2.6.0 |
