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  2. Buffon's needle problem - Wikipedia

    en.wikipedia.org/wiki/Buffon's_needle_problem

    Suppose l > t.In this case, integrating the joint probability density function, we obtain: = = (), where m(θ) is the minimum between ⁠ l / 2 ⁠ sinθ and ⁠ t / 2 ⁠.. Thus, performing the above integration, we see that, when l > t, the probability that the needle will cross at least one line is

  3. Multiple comparisons problem - Wikipedia

    en.wikipedia.org/wiki/Multiple_comparisons_problem

    However, if one considers 100 confidence intervals simultaneously, each with 95% coverage probability, the expected number of non-covering intervals is 5. If the intervals are statistically independent from each other, the probability that at least one interval does not contain the population parameter is 99.4%.

  4. Coupon collector's problem - Wikipedia

    en.wikipedia.org/wiki/Coupon_collector's_problem

    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 ...

  5. PCP theorem - Wikipedia

    en.wikipedia.org/wiki/PCP_theorem

    The PCP theorem states that NP = PCP[O(log n), O(1)],. where PCP[r(n), q(n)] is the class of problems for which a probabilistically checkable proof of a solution can be given, such that the proof can be checked in polynomial time using r(n) bits of randomness and by reading q(n) bits of the proof, correct proofs are always accepted, and incorrect proofs are rejected with probability at least 1/2.

  6. Newton–Pepys problem - Wikipedia

    en.wikipedia.org/wiki/Newton–Pepys_problem

    The Newton–Pepys problem is a probability problem concerning the probability of throwing sixes from a certain number of dice. [1] In 1693 Samuel Pepys and Isaac Newton corresponded over a problem posed to Pepys by a school teacher named John Smith. [2] The problem was: Which of the following three propositions has the greatest chance of success?

  7. Balls into bins problem - Wikipedia

    en.wikipedia.org/wiki/Balls_into_bins_problem

    In the simplest case, if one allocates balls into bins (with =) sequentially one by one, and for each ball one chooses random bins at each step and then allocates the ball into the least loaded of the selected bins (ties broken arbitrarily), then with high probability the maximum load is: [8]

  8. Isolation lemma - Wikipedia

    en.wikipedia.org/wiki/Isolation_lemma

    Then, with probability at least /, there is a unique set in that has the minimum weight among all sets of . It is remarkable that the lemma assumes nothing about the nature of the family F {\displaystyle {\mathcal {F}}} : for instance F {\displaystyle {\mathcal {F}}} may include all 2 n − 1 {\displaystyle 2^{n}-1} nonempty subsets.

  9. Boole's inequality - Wikipedia

    en.wikipedia.org/wiki/Boole's_inequality

    In probability theory, Boole's inequality, also known as the union bound, says that for any finite or countable set of events, the probability that at least one of the events happens is no greater than the sum of the probabilities of the individual events. This inequality provides an upper bound on the probability of occurrence of at least one ...