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	<title>Comments on: Simple approximation to normal distribution</title>
	<atom:link href="http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/feed/" rel="self" type="application/rss+xml" />
	<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/</link>
	<description>The blog of John D. Cook</description>
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		<title>By: Links I&#8217;ve been thinking about &#171; Belligerati</title>
		<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/comment-page-1/#comment-40616</link>
		<dc:creator>Links I&#8217;ve been thinking about &#171; Belligerati</dc:creator>
		<pubDate>Wed, 23 Jun 2010 23:22:13 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=5226#comment-40616</guid>
		<description>[...] just might have a use for a Simple approximation to normal distribution in my work on mortgage [...]</description>
		<content:encoded><![CDATA[<p>[...] just might have a use for a Simple approximation to normal distribution in my work on mortgage [...]</p>
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	<item>
		<title>By: denis</title>
		<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/comment-page-1/#comment-38579</link>
		<dc:creator>denis</dc:creator>
		<pubDate>Mon, 17 May 2010 15:17:52 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=5226#comment-38579</guid>
		<description>Another nice approximation is the piecewise-cubic
    M4 = M1 convolve M1 convolve M1 convolve M1
where M1(x) is the step function: 1 for x in -.5 .. .5, else 0.
Explicitly,
&lt;code&gt;
def M4(x):
    return (
          np.fmax( x+2, 0 ) ** 3
    - 4 * np.fmax( x+1, 0 ) ** 3
    + 6 * np.fmax( x, 0 ) ** 3
    - 4 * np.fmax( x-1, 0 ) ** 3
    +     np.fmax( x-2, 0 ) ** 3
    ) / 6
&lt;/code&gt;

This comes from a lovely paper by I.J.Schoenberg, &quot;On equidistant cubic spline interpolation&quot;, Bulletin AMS, 1971.

cheers
  -- denis</description>
		<content:encoded><![CDATA[<p>Another nice approximation is the piecewise-cubic<br />
    M4 = M1 convolve M1 convolve M1 convolve M1<br />
where M1(x) is the step function: 1 for x in -.5 .. .5, else 0.<br />
Explicitly,<br />
<code><br />
def M4(x):<br />
    return (<br />
          np.fmax( x+2, 0 ) ** 3<br />
    - 4 * np.fmax( x+1, 0 ) ** 3<br />
    + 6 * np.fmax( x, 0 ) ** 3<br />
    - 4 * np.fmax( x-1, 0 ) ** 3<br />
    +     np.fmax( x-2, 0 ) ** 3<br />
    ) / 6<br />
</code></p>
<p>This comes from a lovely paper by I.J.Schoenberg, &#8220;On equidistant cubic spline interpolation&#8221;, Bulletin AMS, 1971.</p>
<p>cheers<br />
  &#8212; denis</p>
]]></content:encoded>
	</item>
	<item>
		<title>By: Carnival of Mathematics #65 &#171; Maxwell&#8217;s Demon</title>
		<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/comment-page-1/#comment-37898</link>
		<dc:creator>Carnival of Mathematics #65 &#171; Maxwell&#8217;s Demon</dc:creator>
		<pubDate>Fri, 07 May 2010 17:39:51 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=5226#comment-37898</guid>
		<description>[...] for more practical things, but to do this we often need to make approximations, for example of the normal distribution.  Code can also provide elegant ways to construct mathematical objects, such as a collection of [...]</description>
		<content:encoded><![CDATA[<p>[...] for more practical things, but to do this we often need to make approximations, for example of the normal distribution.  Code can also provide elegant ways to construct mathematical objects, such as a collection of [...]</p>
]]></content:encoded>
	</item>
	<item>
		<title>By: Jan</title>
		<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/comment-page-1/#comment-37762</link>
		<dc:creator>Jan</dc:creator>
		<pubDate>Wed, 05 May 2010 05:48:20 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=5226#comment-37762</guid>
		<description>Shucks... I tried to post images of these in the above.  I know the first is correct.  I believe the second, but I&#039;m doing from memory.  To see these, check out the links

http://bilge.pyrate.mailbolt.com/blogBeginningFriendship7Day2010/PolyaGaussian.png

and

http://bilge.pyrate.mailbolt.com/blogBeginningFriendship7Day2010/AludaatAlodatGaussian.png</description>
		<content:encoded><![CDATA[<p>Shucks&#8230; I tried to post images of these in the above.  I know the first is correct.  I believe the second, but I&#8217;m doing from memory.  To see these, check out the links</p>
<p><a href="http://bilge.pyrate.mailbolt.com/blogBeginningFriendship7Day2010/PolyaGaussian.png" rel="nofollow">http://bilge.pyrate.mailbolt.com/blogBeginningFriendship7Day2010/PolyaGaussian.png</a></p>
<p>and</p>
<p><a href="http://bilge.pyrate.mailbolt.com/blogBeginningFriendship7Day2010/AludaatAlodatGaussian.png" rel="nofollow">http://bilge.pyrate.mailbolt.com/blogBeginningFriendship7Day2010/AludaatAlodatGaussian.png</a></p>
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	<item>
		<title>By: Jan</title>
		<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/comment-page-1/#comment-37761</link>
		<dc:creator>Jan</dc:creator>
		<pubDate>Wed, 05 May 2010 05:46:29 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=5226#comment-37761</guid>
		<description>There&#039;s a history of these ... There&#039;s a famous approximation by Polya which I first learned of in the Chmura Kraemer and Thiemann book &lt;em&gt;How Many Subjects?&lt;/em&gt;:


and an improvement in, I believe, a Master&#039;s thesis by one Aludaat-Alodat:</description>
		<content:encoded><![CDATA[<p>There&#8217;s a history of these &#8230; There&#8217;s a famous approximation by Polya which I first learned of in the Chmura Kraemer and Thiemann book <em>How Many Subjects?</em>:</p>
<p>and an improvement in, I believe, a Master&#8217;s thesis by one Aludaat-Alodat:</p>
]]></content:encoded>
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	<item>
		<title>By: Interesting Links - Statistics Teaching</title>
		<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/comment-page-1/#comment-37571</link>
		<dc:creator>Interesting Links - Statistics Teaching</dc:creator>
		<pubDate>Sun, 02 May 2010 13:28:02 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=5226#comment-37571</guid>
		<description>[...] Approximation of the Normal Curve Using Cosine &#8211; John Cook at The Endeavour describes an approximation from an old paper in Psychometrika. This could be a nice exploration in a calculus or mathematical statistics course. [...]</description>
		<content:encoded><![CDATA[<p>[...] Approximation of the Normal Curve Using Cosine &#8211; John Cook at The Endeavour describes an approximation from an old paper in Psychometrika. This could be a nice exploration in a calculus or mathematical statistics course. [...]</p>
]]></content:encoded>
	</item>
	<item>
		<title>By: vonjd</title>
		<link>http://www.johndcook.com/blog/2010/04/29/simple-approximation-to-normal-distribution/comment-page-1/#comment-37417</link>
		<dc:creator>vonjd</dc:creator>
		<pubDate>Fri, 30 Apr 2010 10:17:01 +0000</pubDate>
		<guid isPermaLink="false">http://www.johndcook.com/blog/?p=5226#comment-37417</guid>
		<description>Another interesting paper in this context:
http://www.jiem.org/index.php/jiem/article/download/60/27</description>
		<content:encoded><![CDATA[<p>Another interesting paper in this context:<br />
<a href="http://www.jiem.org/index.php/jiem/article/download/60/27" rel="nofollow">http://www.jiem.org/index.php/jiem/article/download/60/27</a></p>
]]></content:encoded>
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