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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
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%.
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 ...
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.
Given a claimed solution x with length n, which might be false, the prover produces a proof π which states x solves L (x ∈ L, the proof is a string ∈ Σ ∗). And the verifier is a randomized oracle Turing Machine V (the verifier) that checks the proof π for the statement that x solves L (or x ∈ L) and decides whether to accept the ...
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 ...
(All the bounds hold with probability at least / for any constant >.) Note that for m > n log n {\displaystyle m>n\log n} , the random allocation process gives only the maximum load of m n + O ( log log n ) {\displaystyle {\frac {m}{n}}+O\left(\log \log n\right)} with high probability, so the improvement between these two processes ...
In others words, the sample complexity (,,) defines the rate of consistency of the algorithm: given a desired accuracy and confidence , one needs to sample (,,) data points to guarantee that the risk of the output function is within of the best possible, with probability at least .