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Each time, a single ball is placed into one of the bins. After all balls are in the bins, we look at the number of balls in each bin; we call this number the load on the bin. The problem can be modelled using a Multinomial distribution, and may involve asking a question such as: What is the expected number of bins with a ball in them? [1]
An analogy for the working of the latter version is to sort a deck of cards by throwing the deck into the air, picking the cards up at random, and repeating the process until the deck is sorted. In a worst-case scenario with this version, the random source is of low quality and happens to make the sorted permutation unlikely to occur.
In probability theory, the coupon collector's problem refers to mathematical analysis of "collect all coupons and win" contests. It asks the following question: if each box of a given product (e.g., breakfast cereals) contains a coupon, and there are n different types of coupons, what is the probability that more than t boxes need to be bought ...
In probability and statistics, an urn problem is an idealized mental exercise in which some objects of real interest (such as atoms, people, cars, etc.) are represented as colored balls in an urn or other container. One pretends to remove one or more balls from the urn; the goal is to determine the probability of drawing one color or another ...
The probability of taking a particular item at a particular draw is equal to its fraction of the total "weight" of all items that have not yet been taken at that moment. The weight of an item depends only on its kind (e.g., color). The total number n of items to take is fixed and independent of which items happen to be taken first.
Write down the numbers from 1 through N. Pick a random number k between one and the number of unstruck numbers remaining (inclusive). Counting from the low end, strike out the kth number not yet struck out, and write it down at the end of a separate list. Repeat from step 2 until all the numbers have been struck out.
The probability that all prisoners find their numbers is the product of the single probabilities, which is ( 1 / 2 ) 100 ≈ 0.000 000 000 000 000 000 000 000 000 0008, a vanishingly small number. The situation appears hopeless.
The Rademacher distribution, which takes value 1 with probability 1/2 and value −1 with probability 1/2. The binomial distribution, which describes the number of successes in a series of independent Yes/No experiments all with the same probability of success.