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		<id>http://stab.st-andrews.ac.uk/wiki/index.php?action=history&amp;feed=atom&amp;title=Maximum_Likelihood</id>
		<title>Maximum Likelihood - Revision history</title>
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		<updated>2026-05-25T20:39:07Z</updated>
		<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>http://stab.st-andrews.ac.uk/wiki/index.php?title=Maximum_Likelihood&amp;diff=1648&amp;oldid=prev</id>
		<title>Rf: Created page with &quot;= Introduction =  This is a very widely used mathematical (probabilistic) approach used when trying to build a model from real-world observations.  A key aspect of the approac...&quot;</title>
		<link rel="alternate" type="text/html" href="http://stab.st-andrews.ac.uk/wiki/index.php?title=Maximum_Likelihood&amp;diff=1648&amp;oldid=prev"/>
				<updated>2017-05-09T14:25:08Z</updated>
		
		<summary type="html">&lt;p&gt;Created page with &amp;quot;= Introduction =  This is a very widely used mathematical (probabilistic) approach used when trying to build a model from real-world observations.  A key aspect of the approac...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;= Introduction =&lt;br /&gt;
&lt;br /&gt;
This is a very widely used mathematical (probabilistic) approach used when trying to build a model from real-world observations.&lt;br /&gt;
&lt;br /&gt;
A key aspect of the approach is that of conditional probability, and we decide to take the real-world observations and say that their probability is in actual fact conditional on certain values that parameters that our notional model take.&lt;br /&gt;
&lt;br /&gt;
Although probabilistic, and made up of probabilities, the likelihood is not itself a formal probability because all its possibilities will not add up to 1. Therefore it is often better to talk of it as a score. This aspect is also a reason why likelihood ratios are popular, because its exact nature disappears in the division.&lt;br /&gt;
&lt;br /&gt;
= Formula =&lt;br /&gt;
&lt;br /&gt;
&amp;lt;math&amp;gt;&lt;br /&gt;
    f(x_1,x_2,\ldots,x_n\mid\theta) = f(x_1\mid \theta)\times f(x_2|\theta) \times \cdots \times  f(x_n\mid \theta).&lt;br /&gt;
&amp;lt;/math&amp;gt;&lt;br /&gt;
&lt;br /&gt;
Now we look at this function from a different perspective by considering the observed values &amp;#039;&amp;#039;x&amp;#039;&amp;#039;&amp;lt;sub&amp;gt;1&amp;lt;/sub&amp;gt;, &amp;#039;&amp;#039;x&amp;#039;&amp;#039;&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt;, …, &amp;#039;&amp;#039;x&amp;#039;&amp;#039;&amp;lt;sub&amp;gt;&amp;#039;&amp;#039;n&amp;#039;&amp;#039;&amp;lt;/sub&amp;gt; to be fixed &amp;quot;parameters&amp;quot; of this function, whereas &amp;#039;&amp;#039;θ&amp;#039;&amp;#039; will be the function&amp;#039;s variable and allowed to vary freely; this same function will be called the &lt;br /&gt;
&amp;lt;math&amp;gt;&lt;br /&gt;
  \mathcal{L}(\theta\,;\,x_1,\ldots,x_n) = f(x_1,x_2,\ldots,x_n\mid\theta) = \prod_{i=1}^n f(x_i\mid\theta).&lt;br /&gt;
&amp;lt;/math&amp;gt;&lt;/div&gt;</summary>
		<author><name>Rf</name></author>	</entry>

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