features f1, f2, …, fn from the positive or negative set is given by: Pr{f1,f2…fn|LR}=Pr{f1|LR}×Pr{f2|LR}×…×Pr{fn|LR},Pr{f1,f2…fn|NR}=Pr{f1|NR}×Pr{f2|NR}×…×Pr{fn|NR}We classified according to the maximal posterior probability and generated a score that was the ratio of the two posterior probabilities Pr{LR|f1,f2,…fn)=Pr{f1,f2,…fn|LR}∗Pr{LR}Pr{f1,f2,…fn}, Pr{NR|f1,f2,…fn)=Pr{f1,f2,…fn|NR}∗Pr{NR}Pr{f1,f2,…fn}Since when we have a new patient that we need to classify, we usually patch a few neurons, and we know a priori that these all belong to the same patient, we can use this information to improve prediction. We therefore multiply the posterior probabilities for classification of a few neurons, take the ratio and classify according to this score.
