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In mathematics, a combination is a selection of items from a set that has distinct members, such that the order of selection does not matter (unlike permutations).For example, given three fruits, say an apple, an orange and a pear, there are three combinations of two that can be drawn from this set: an apple and a pear; an apple and an orange; or a pear and an orange.
This "equal probability" assumption is a deeply rooted intuition. [26] People strongly tend to think probability is evenly distributed across as many unknowns as are present, whether or not that is true in the particular situation under consideration. [27] The problem continues to attract the attention of cognitive psychologists.
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 ...
What is the probability that using these keys you can open all n boxes, where you use a found key to open the box it belongs to and repeat. The mathematical statement of this problem is as follows: pick a random permutation on n elements and k values from the range 1 to n, also at random, call these marks. What is the probability that there is ...
Items are taken independently of each other. Whether one item is taken is independent of whether another item is taken. Whether one item is taken before, after, or simultaneously with another item is irrelevant. The probability of taking a particular item is proportional to its "weight". The weight of an item depends only on its kind (e.g., color).
A powerful balls-into-bins paradigm is the "power of two random choices [2]" where each ball chooses two (or more) random bins and is placed in the lesser-loaded bin. This paradigm has found wide practical applications in shared-memory emulations, efficient hashing schemes, randomized load balancing of tasks on servers, and routing of packets ...
Bloom filters are a way of compactly representing a set of items. It is common to try to compute the size of the intersection or union between two sets. Bloom filters can be used to approximate the size of the intersection and union of two sets. For two Bloom filters of length m, their counts, respectively can be estimated as
[7] [8] A naive algorithm is the draw-by-draw algorithm where at each step we remove the item at that step from the set with equal probability and put the item in the sample. We continue until we have a sample of desired size . The drawback of this method is that it requires random access in the set.