Any help, hints, direction etc. on this appreciated:
I have a vector of measurements F, and two feature models L and R which
give the pdf of a measurement Fi for that model i.e. P(Fi|Li) and
P(Fi|Ri). Denoting the probability that there is a feature L at position
i in the vector as P(Li) and similarly feature R at position j as P(Rj)
I am interested in obtaining
P(Li,Rj|F)
i.e. the probability that there is feature L at position i and feature R
at position j, given the vector F. The assumptions I will make are
P(Li|F)=P(Li|Fi) and P(Ri|F)=P(Ri|Fi)
i.e. the feature depends only on local value of the vector. What I will
*not* assume is that P(Li) and P(Rj) are independent. i.e. the position
of feature L influences that position of feature R.
What I am looking for is an expression for P(Li,Rj|F) derived only from
P(Fi|Li), P(Fj|Rj) and P(Li|Rj) or similar.
P(Li,Rj|F)
=P(Li|Rj,F)P(Ri,F)
=P(Li|Rj,F)P(Ri,Fi)
...?
Knowing that P(Li|F)=P(Li|Fi) can I simplify P(Li|Rj,F) further?
Mark
--
________________________________________________________________________
Mark Everingham Phone: +44 117 9545249
Room 1.15 Fax: +44 117 9545208
Merchant Venturers Building Email: [EMAIL PROTECTED]
University of Bristol WWW: http://www.cs.bris.ac.uk/~everingm/
Bristol BS8 1UB, UK
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