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In our prototype, we compute p(s|f) based on Bayesian formula: p(s|f)=p(f|s)×p s)/p(f). p(f|s) is the posterior probability that shows how likely a fault f occurred if a symptom s observed.
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We measured the delay times in the prototype we developed.
For our iPod prototype, we made the latter concession.
When we compare unfamiliar things to a prototype, we notice those things most representative of the prototype.
We compute.
How do we compute such things?
We compute φ(T1k, T2k, Ok).
We compute and.
We computed the remaining BFs as follows.
We computed two models.
Cluster step: Given a set of prototypes, assign genes to prototypes and find their model parameters θ Prototype step: Given assignment of genes to prototypes (clusters), improve prototypes We now elaborate on each step: Cluster step For each gene g we find the optimal parameters θ g and typical prototype pg.
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