One approach to assessment Want to compute the probability that a prediction is good based on properties of the model For a given score of the model e.g. Q-score-more on this later), use a training set of known examples together with Bayes'rule P(AB) =P(AA B/P(B)=P(AP(BA)RP(AP(BA)+ P(AP(B !A)y Assume probability of a good Vs a bad model is the same . e P(a)=P(A) where a good model; !a =bad model; B=Q-score P(good Q-score)=P(Q-scorel good)/P(Q-score good)+P(Q-score bad)] good models bad models Q -score Sanchez, R, and A sali. "Large-scale Protein Structure Modeling of The Saccharomyces Cerevisiae Genome Proc Nat/ Acad SciU SA. 95, no. 23(10 November 1998 ) 13597-602One approach to assessment Want to compute the probability that a prediction is good, based on properties of the model For a given score of the model (e.g. Q-score - more on this later), use a training set of known examples, together with Bayes’ rule P(A|B) = P(A ^ B)/P(B) = P(A)P(B|A)/{P(A)P(B|A) + P(!A)P(B|!A)} Assume probability of a good vs. a bad model is the same, i.e. P(A) = P(!A) where A = good model; !A = bad model; B = Q-score P(good|Q-score) = P(Q-score|good)/{P(Q-score|good) + P(Q-score|bad)} Prob. Q-score good models bad models Sanchez, R, and A Sali. "Large-scale Protein Structure Modeling of The Saccharomyces Cerevisiae Genome." Proc Natl Acad Sci U S A. 95, no. 23 (10 November 1998): 13597-602