Rich
At 07:42 AM 6/7/99 -0500, Rich Neapolitan wrote:
>My experience is that if you use an equivalent sample size, the variance in
>the relative frequency is unchanged when you propagate down the network
>(which makes sense intuitively). However, it increases when you propagate
>up and the increase is relative to the number of paths from which
>information is coming up. For example, if we are instantiating children,
>two such children results in a bigger increase than one, three bigger than
>two, and so on. This also makes sense when you think about the proportion
>of the equivalent sample that goes into the calculation.
>
>Rich
>
>
>At 01:02 PM 6/6/99 -0600, Bob Welch wrote:
>>Dongsong :
>>
>>If you start with an imprecise or "uninformative" prior, then updating with
>>new evidence will generate a more precise or informed posterior -- how much
>>more informed depends on the likelihood's ability to discriminate.
>>
>>But Bayesian updating does not necessarily result in more precision -- the
>>prior could have been very precise and the evidence a surprise given that
>>prior -- this results in a less precise posterior.
>>
>>Eventually (as more and more "surprising" evidence is observed), the
>>posterior becomes more precise as it concludes that the original priors were
>>incorrect.
>>
>>Bob
>>
>>-----Original Message-----
>>From: Dongsong Zeng <zengdong@pilot.msu.edu>
>>To: uai@CS.ORST.EDU <uai@CS.ORST.EDU>
>>Date: Friday, June 04, 1999 12:28 PM
>>Subject: BN prior prob
>>
>>
>>>Hi,
>>>
>>>if my Belief Networks starts with unprecise prior probabilities, can I get
>>>precise results? How and Why? Any advices are highly appreciated.
>>>
>>>Thank you very much.
>>>
>>>
>>> --
>>>Dongsong Zeng
>>>
>>>Email:zengdong@pilot.msu.edu
>>
>>
>
>