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From: orourke@utstat.toronto.edu (Keith O'Rourke)
Subject: Re: Fuzzy logic compared to probability
Message-ID: <Do7MCv.J2p@utstat.toronto.edu>
Organization: U of Toronto Statistics
References: <312B60FB.41C67EA6@colorado.edu> <DnsyEI.6u5@decan.com> <4hvf2e$k3m@mercury.dur.ac.uk> <Do2uHv.1EG@decan.com> <96Mar11.153112edt.1217@neuron.ai.toronto.edu>
Date: Wed, 13 Mar 1996 14:17:19 GMT
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Xref: glinda.oz.cs.cmu.edu comp.ai.fuzzy:7007 sci.stat.math:9756

>Article 8643 of 8647 (sci.stat.math) (32 lines) Mon Mar 11 15:31:27 1996
>Subject: Re: Fuzzy logic compared to probability
>From: radford@cs.toronto.edu (Radford Neal)


>There might, however, be an objective summary of the data, such
>as the likelihood function.

Only if the assumed probability model is exactly correct ... 

But more importantly why do we need (these days) a summary other 
than the data themselves or a large random sub-sample???

>This is an advantage!  It means that you actually know what the
>indications of uncertainty (probabilities) for the universals mean,
>by analogy with what probabilities for particulars mean.

But analogies can sometimes be very dis-advantageous.  
(There is a word for a bad metaphor - metachrysis (not in my dictionary) 
especially to denote this danger)

Keith O'Rourke
The Toronto Hosp.

