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</script></head><body><div id="package-header"><ul class="links" id="page-menu"><li><a href="src/Statistics-Sample-KernelDensity.html">Source</a></li><li><a href="index.html">Contents</a></li><li><a href="doc-index.html">Index</a></li></ul><p class="caption">statistics-0.10.2.0: A library of statistical types, data, and functions</p></div><div id="content"><div id="module-header"><table class="info"><tr><th>Portability</th><td>portable</td></tr><tr><th>Stability</th><td>experimental</td></tr><tr><th>Maintainer</th><td>bos@serpentine.com</td></tr><tr><th>Safe Haskell</th><td>None</td></tr></table><p class="caption">Statistics.Sample.KernelDensity</p></div><div id="table-of-contents"><p class="caption">Contents</p><ul><li><a href="#g:1">Estimation functions
</a></li><li><a href="#g:2">References
</a></li></ul></div><div id="description"><p class="caption">Description</p><div class="doc"><p>Kernel density estimation. This module provides a fast, robust,
non-parametric way to estimate the probability density function of
a sample.
</p><p>This estimator does not use the commonly employed "Gaussian rule
of thumb". As a result, it outperforms many plug-in methods on
multimodal samples with widely separated modes.
</p></div></div><div id="synopsis"><p id="control.syn" class="caption expander" onclick="toggleSection('syn')">Synopsis</p><ul id="section.syn" class="hide" onclick="toggleSection('syn')"><li class="src short"><a href="#v:kde">kde</a> :: <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Data-Int.html#t:Int">Int</a> -> <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a> -> (<a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>, <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>)</li><li class="src short"><a href="#v:kde_">kde_</a> :: <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Data-Int.html#t:Int">Int</a> -> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a> -> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a> -> <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a> -> (<a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>, <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>)</li></ul></div><div id="interface"><h1 id="g:1">Estimation functions
</h1><div class="top"><p class="src"><a name="v:kde" class="def">kde</a><a href="src/Statistics-Sample-KernelDensity.html#kde" class="link">Source</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Data-Int.html#t:Int">Int</a></td><td class="doc"><p>The number of mesh points to use in the uniform discretization
of the interval <code>(min,max)</code>. If this value is not a power of
two, then it is rounded up to the next power of two.
</p></td></tr><tr><td class="src">-> <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a></td><td class="doc empty"> </td></tr><tr><td class="src">-> (<a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>, <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>)</td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Gaussian kernel density estimator for one-dimensional data, using
the method of Botev et al.
</p><p>The result is a pair of vectors, containing:
</p><ul><li> The coordinates of each mesh point. The mesh interval is chosen
to be 20% larger than the range of the sample. (To specify the
mesh interval, use <code><a href="Statistics-Sample-KernelDensity.html#v:kde_">kde_</a></code>.)
</li><li> Density estimates at each mesh point.
</li></ul></div></div><div class="top"><p class="src"><a name="v:kde_" class="def">kde_</a><a href="src/Statistics-Sample-KernelDensity.html#kde_" class="link">Source</a></p><div class="subs arguments"><p class="caption">Arguments</p><table><tr><td class="src">:: <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Data-Int.html#t:Int">Int</a></td><td class="doc"><p>The number of mesh points to use in the uniform discretization
of the interval <code>(min,max)</code>. If this value is not a power of
two, then it is rounded up to the next power of two.
</p></td></tr><tr><td class="src">-> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a></td><td class="doc"><p>Lower bound (<code>min</code>) of the mesh range.
</p></td></tr><tr><td class="src">-> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a></td><td class="doc"><p>Upper bound (<code>max</code>) of the mesh range.
</p></td></tr><tr><td class="src">-> <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a></td><td class="doc empty"> </td></tr><tr><td class="src">-> (<a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>, <a href="/usr/share/doc/libghc-vector-doc/html/Data-Vector-Unboxed.html#t:Vector">Vector</a> <a href="/usr/share/doc/ghc-doc/html/libraries/base-4.6.0.1/Prelude.html#t:Double">Double</a>)</td><td class="doc empty"> </td></tr></table></div><div class="doc"><p>Gaussian kernel density estimator for one-dimensional data, using
the method of Botev et al.
</p><p>The result is a pair of vectors, containing:
</p><ul><li> The coordinates of each mesh point.
</li><li> Density estimates at each mesh point.
</li></ul></div></div><h1 id="g:2">References
</h1><div class="doc"><p>Botev. Z.I., Grotowski J.F., Kroese D.P. (2010). Kernel density
estimation via diffusion. <em>Annals of Statistics</em>
38(5):2916–2957. <a href="http://arxiv.org/pdf/1011.2602">http://arxiv.org/pdf/1011.2602</a>
</p></div></div></div><div id="footer"><p>Produced by <a href="http://www.haskell.org/haddock/">Haddock</a> version 2.13.2</p></div></body></html>
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