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P(A|B) may or may not be equal to P(A), i.e., the unconditional probability or absolute probability of A. If P(A|B) = P(A), then events A and B are said to be independent: in such a case, knowledge about either event does not alter the likelihood of each other. P(A|B) (the conditional probability of A given B) typically differs from P(B|A).
P( at least one estimation is bad) = 0.05 ≤ P( A 1 is bad) + P( A 2 is bad) + P( A 3 is bad) + P( A 4 is bad) + P( A 5 is bad) One way is to make each of them equal to 0.05/5 = 0.01, that is 1%. In other words, you have to guarantee each estimate good to 99%( for example, by constructing a 99% confidence interval) to make sure the total ...
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P (A), the prior, is the initial degree of belief in A. P (A | B), the posterior, is the degree of belief after incorporating news that B is true. the quotient P(B | A) / P(B) represents the support B provides for A. For more on the application of Bayes' theorem under the Bayesian interpretation of probability, see Bayesian inference.
Faulhaber's formula concerns expressing the sum of the p-th powers of the first n positive integers = = + + + + as a (p + 1)th-degree polynomial function of n.. The first few examples are well known.
Assume that there is a counterexample: an integer n ≥ 2 such that there is no prime p with n < p < 2n. If 2 ≤ n < 427, then p can be chosen from among the prime numbers 3, 5, 7, 13, 23, 43, 83, 163, 317, 631 (each being the largest prime less than twice its predecessor) such that n < p < 2n. Therefore, n ≥ 427.
This article is written for those who want to get better at using price to earnings ratios (P/E ratios). To keep it...
The coefficient, r k (B) of x k in the rook polynomial R B (x) is the number of ways k rooks, none of which attacks another, can be arranged in the squares of B. For any board B, there is a complementary board ′ consisting of the squares of the rectangular board that are not in B.