Question

I’m working on a Bayesian probability project, in which I need to adjust probabilities based on new information. I have yet to find an efficient way to do this. What I’m trying to do is start with an equal probability list for distinct scenarios. Ex.
There are 6 people: E, T, M, Q, L, and Z, and their initial respective probabilities of being chosen are represented in

myList=[.1667, .1667, .1667, .1667, .1667, .1667]

New information surfaces that people in the first third alphabetically have a collective 70% chance of being chosen. A new list is made, sorted alphabetically by name (E, L, M, Q, T, Z), that just includes the new information. (.7/.333=2.33, .3/.667=.45)

    newList=[2.33, 2.33, .45, .45, .45, .45)

I need a way to order the newList the same as myList so I can multiply the right values in list comprehension, and reach the adjust probabilities. Having a single consistent order is important because the process will be repeated several times, each with different criteria (vowels, closest to P, etc), and in a list with about 1000 items.
Each newList could instead be a newDictionary, and then once the adjustment criteria are created they could be ordered into a list, but transforming multiple dictionaries seems inefficient. Is it? Is there a simple way to do this I’m entirely missing?

Thanks!

Answer 1

Try storing your info as a list of tuples:

bayesList = [('E', 0.1667), ('M', 0.1667), ...]

your list comprehension can be along the lines of

newBayes = [(person, prob * normalizeFactor) for person, prob in bayesList]

where you’ve normalizeFactor was calculated before setting up your list comprehension