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	<title>Comments on: An Intuitive (and Short) Explanation of Bayes&#039; Theorem</title>
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	<description>Learn Right, Not Rote.</description>
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		<title>By: Interesting -The Carbohydrate Hypothesis of Obesity: a Critical Examination - Page 11 &#124; Mark's Daily Apple Health and Fitness Forum page 11</title>
		<link>http://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/#comment-78925</link>
		<dc:creator>Interesting -The Carbohydrate Hypothesis of Obesity: a Critical Examination - Page 11 &#124; Mark's Daily Apple Health and Fitness Forum page 11</dc:creator>
		<pubDate>Tue, 15 May 2012 20:47:07 +0000</pubDate>
		<guid isPermaLink="false">http://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/#comment-78925</guid>
		<description>[...]   What about all the single digit body fat people you don&#039;t know? Don&#039;t commit base rate fallacy.  An Intuitive (and Short) Explanation of Bayes&#039; Theorem &#124; BetterExplained        Originally Posted by dboxing    Where is the epidemic of obesity in Holland (where I was [...]</description>
		<content:encoded><![CDATA[<p>[...]   What about all the single digit body fat people you don&#039;t know? Don&#039;t commit base rate fallacy.  An Intuitive (and Short) Explanation of Bayes&#039; Theorem | BetterExplained        Originally Posted by dboxing    Where is the epidemic of obesity in Holland (where I was [...]</p>
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		<title>By: elmira komijani</title>
		<link>http://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/#comment-77869</link>
		<dc:creator>elmira komijani</dc:creator>
		<pubDate>Sat, 12 May 2012 12:45:29 +0000</pubDate>
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		<description>Hello guys;
could you help me to solve this problem as soon as possible?
Transplant operations for hearts have the risk that the body may reject
the organ. A new test has been developed to detect early warning signs
that the body may be rejecting the heart. However, the test is not
perfect. When the test is conducted on someone whose heart will be
rejected, approximately two out of ten tests will be negative (the test is
wrong). When the test is conducted on a person whose heart will not
be rejected, 10% will show a positive test result (another incorrect
result). Doctors know that in about 50% of heart transplants the body
tries to reject the organ.
*Suppose the test was performed on my mother and the test is positive
(indicating early warning signs of rejection). What is the probability that the
body is attempting to reject the heart?
*Suppose the test was performed on my mother and the test is negative
(indicating no signs of rejection). What is the probability that the body is
attempting to reject the heart?</description>
		<content:encoded><![CDATA[<p>Hello guys;<br />
could you help me to solve this problem as soon as possible?<br />
Transplant operations for hearts have the risk that the body may reject<br />
the organ. A new test has been developed to detect early warning signs<br />
that the body may be rejecting the heart. However, the test is not<br />
perfect. When the test is conducted on someone whose heart will be<br />
rejected, approximately two out of ten tests will be negative (the test is<br />
wrong). When the test is conducted on a person whose heart will not<br />
be rejected, 10% will show a positive test result (another incorrect<br />
result). Doctors know that in about 50% of heart transplants the body<br />
tries to reject the organ.<br />
*Suppose the test was performed on my mother and the test is positive<br />
(indicating early warning signs of rejection). What is the probability that the<br />
body is attempting to reject the heart?<br />
*Suppose the test was performed on my mother and the test is negative<br />
(indicating no signs of rejection). What is the probability that the body is<br />
attempting to reject the heart?</p>
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		<title>By: Jack Kruse....what is he up to? &#124; Mark's Daily Apple Health and Fitness Forum page</title>
		<link>http://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/#comment-76487</link>
		<dc:creator>Jack Kruse....what is he up to? &#124; Mark's Daily Apple Health and Fitness Forum page</dc:creator>
		<pubDate>Tue, 08 May 2012 07:33:15 +0000</pubDate>
		<guid isPermaLink="false">http://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/#comment-76487</guid>
		<description>[...]          Sounds like a false positive... An Intuitive (and Short) Explanation of Bayes&#039; Theorem &#124; BetterExplained           Reply With Quote                   &#171; Previous Thread &#124; Next Thread [...]</description>
		<content:encoded><![CDATA[<p>[...]          Sounds like a false positive&#8230; An Intuitive (and Short) Explanation of Bayes&#039; Theorem | BetterExplained           Reply With Quote                   &laquo; Previous Thread | Next Thread [...]</p>
]]></content:encoded>
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		<title>By: gerry worts</title>
		<link>http://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/#comment-65425</link>
		<dc:creator>gerry worts</dc:creator>
		<pubDate>Thu, 29 Mar 2012 03:23:41 +0000</pubDate>
		<guid isPermaLink="false">http://betterexplained.com/articles/an-intuitive-and-short-explanation-of-bayes-theorem/#comment-65425</guid>
		<description>Here are two contradictory statements in your article regarding False positives. I quote:

Pr(X&#124;~A) = Chance of a positive test (X) given that you didn’t have cancer (~A). This is a false positive, 9.6% in our case.

The chances of a false positive = chance you don’t have cancer * chance test caught it anyway = 99% * 9.6% = 0.09504

These two interpretations of false positives are often mixed in various texts.
One is Pr(X&#124;~A) and the other is Pr(X∩~A)</description>
		<content:encoded><![CDATA[<p>Here are two contradictory statements in your article regarding False positives. I quote:</p>
<p>Pr(X|~A) = Chance of a positive test (X) given that you didn’t have cancer (~A). This is a false positive, 9.6% in our case.</p>
<p>The chances of a false positive = chance you don’t have cancer * chance test caught it anyway = 99% * 9.6% = 0.09504</p>
<p>These two interpretations of false positives are often mixed in various texts.<br />
One is Pr(X|~A) and the other is Pr(X∩~A)</p>
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