There are two classes of strings of events. E.g.
Both class A and B strings exhibit variability. Many (e.g. 10,000) A and B strings are collected. I collect the observations into a matrix x where columns are the value of each event (12 events in the example above) and the rows are individual strings of events.
Then for each event[i] I can find:
p(A|x[,i]>0) = sum(x[,i]>0 & A)/sum(x[,i]>0)
where the x[,i] notation means column i (ie events i=1 to 12)
sum(x[,i]>0 & A) counts the number of times A occurs when event i >0
sum(x[,i]>0) counts the number of times event i >0
If I do this for all events I can find a probability map which says how likely the string is from class A given the x value is greater than 0.
pmap A: 0,0,.5,.6,.4.,.5,.9,.7,.6,.5,0,0
pmap B=1-A (since each string is either class A or class B)
Now suppose I am presented a new string of events. Does it belong to class A or B? I feel that I should be able to use my probability map to make this decision along with the new string. How likely is it that this string belongs to class A given the probability map? I am thinking: if this likelihood is bigger than some criterion, say A, else say B. Is this idea workable?
Please tell me how to proceed. Thanks very much for any help.