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The conditioning event is interpreted as evidence for the conditioned event. That is, P(A) is the probability of A before accounting for evidence E, and P(A|E) is the probability of A after having accounted for evidence E or after having updated P(A). This is consistent with the frequentist interpretation, which is the first definition given above.
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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 ...
In this case, there are two values for which f is maximal: (n + 1) p and (n + 1) p − 1. M is the most probable outcome (that is, the most likely, although this can still be unlikely overall) of the Bernoulli trials and is called the mode. Equivalently, M − p < np ≤ M + 1 − p. Taking the floor function, we obtain M = floor(np). [note 1]
This article is written for those who want to get better at using price to earnings ratios (P/E ratios). To keep it...
When heads occurs, tails can't occur, or p (heads and tails) = 0, so the outcomes are also mutually exclusive. Another example of events being collectively exhaustive and mutually exclusive at same time are, event "even" (2,4 or 6) and event "odd" (1,3 or 5) in a random experiment of rolling a six-sided die. These both events are mutually ...
Probability generating functions obey all the rules of power series with non-negative coefficients. In particular, () =, where () =, < (), x approaching 1 from below, since the probabilities must sum to one.
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