Still on the topic of probability, I came across yet another interesting problem in the E.T Jaynes book and it goes as follows

Suppose an urn contains balls of color , of color of color . We draw balls without replacement; what is the probability that we have at least one of each color

For a simple illustration, say we have an urn with 4 balls of two different colors, say black and white. What would be the probability of picking three balls having at least one white and one black.

The probability would look something like this where is the probability and is the current state of information about the urn. Then we have

The probability seems quite tedious especially if the number of balls in the urn increase, and the number of selection also increases. But we can notice something similar in the probabilities. They all have 2 Whites,or 2 Balls in different arrangements. So all we need do is find the probability of selecting Two black and two whites and then multiply by a certain factor to account for other arrangements.

We start with two whites first. Picking white on the first and second draws while picking black on the third draw.

now that we have individual conditional probabilities, we can now the probability in terms of the state of the information of the urn

the equation above can be written in more elegant form as

this can then be extended to the other case of two blacks and white in the same order

now since we have established the other probabilities are simply just rearrangements of these two. Then we can say that

The equation then reduces to

The equation looks quite elegant, now suppose that we had three colors instead. 3 Black, 3 White and 3 Red Balls and we select 4 random balls. The probability easily becomes

Now suppose for the same scenario above that we are supposed to select 5 balls instead. The probability simply also becomes

From the two scenarios above we see a general pattern occurring. First the draws is partitioned into parts, where and

Now given balls with each of color we can then write the probability of selecting balls with at least one of each as

PHEW!! That was quite interesting!

Now the second part of the question proceeds as follows

Supposing that and all how many do we need to draw in order to have at least a % probability for getting a full set.

stated mathematically this simply means,

find minimum such that

where

and

This problem would lend itself better to programming based solution as any trick employed would require advanced knowledge of Counting(Combinatorics)

Forthe solution, I Used Haskell and got the following output (The code is placed at the end of the blog post)

```
5 draws => 4.7197417357322205e-2
6 draws => 0.14159225207196663
7 draws => 0.26280380119418045
8 draws => 0.39045136177421097
9 draws => 0.5107424115832719
10 draws => 0.6164726091135438
11 draws => 0.7051322262819913
12 draws => 0.7770125811341809
13 draws => 0.8338240294168358
14 draws => 0.8778255150191284
15 draws => 0.9113323783701572
```

Therefore our answer is **15** draws

The Haskell Code is given below [git – https://github.com/igbokwedaniel/bernoulli_urn]

```
import qualified Data.List as List
import qualified Data.Map as Map
import qualified Data.Maybe as May
import Control.Monad
--Defining The Factorial nCr = n!/r!(n-r)!
factorial' :: (Integral a) => a -> a -> a
factorial' _ 0 = 1
factorial' 0 _ = 0
factorial' n k = factorial' (n-1) (k-1) * n `div` k
-- defining the function which returns all the possible combinations,
-- I'm explicitly assuming the k = 5 colors here
sumFinder' :: (Integral a) => a -> a -> Maybe a
sumFinder' 0 _ = Nothing
sumFinder' _ 0 = Nothing
sumFinder' n m = Just $ Map.foldrWithKey f 0 (getMap' n m)
where
f k a len = len + ((folder' k n)*a)
--generating the unique mapfrp
getMap' :: (Integral a) => a-> a -> Map.Map [a] a
getMap' n m = Map.fromListWith (+) $ zip (getAllSums' m n) (cycle [1])
getAllSums' :: (Integral a) => a -> a -> [[a]]
getAllSums' m n = [ List.sort [a,b,c,d,e] | a <- [1..n], b <- [1..n], c <- [1..n], d <- [1..n], e <- [1..n], a+b+c+d+e == m]
folder' :: (Integral a) => [a] -> a -> a
folder' arr n = List.foldl (*) 1 $ map (factorial' n) arr
toFractional' :: (Num b, Integral a) => Maybe a -> b
toFractional' m = fromIntegral $ (May.fromJust m)
probability' :: (Integral a, Fractional b) => a -> a -> b
probability' n m = x/y
where
x = toFractional' (sumFinder' n m)
y = fromIntegral (factorial' (5*n) m)
getAllProbabilities' :: (Integral a, Fractional b, Ord b) => a -> b -> [(a,b)]
getAllProbabilities' n lim = genString' n [5..(n*5)] lim
genString' :: (Integral a, Fractional b, Ord b) => a -> [a] -> b -> [(a,b)]
genString' n (x:xs) lim
| (probability' n x) > lim = [(x ,(probability' n x))]
| otherwise = [(x ,(probability' n x))] ++ (genString' n xs lim)
main = putStrLn $ show (getAllProbabilities' 10 0.9)
```

So amazing!!!!!! I understood not a lick of anything here but it was a super great read!!!

I must confess I do not understand all the magic that happened up here at a glance but I am amazed at the fact there is still more to discover in the world of probability.

I am definitely going to read this through again.

Thank you Igbokwe, Cheers!