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The odds strategy is the rule to observe the events one after the other and to stop on the first interesting event from index s onwards (if any), where s is the stopping threshold of output a. The importance of the odds strategy, and hence of the odds algorithm, lies in the following odds theorem.
Graphs of probability P of not observing independent events each of probability p after n Bernoulli trials vs np for various p.Three examples are shown: Blue curve: Throwing a 6-sided die 6 times gives a 33.5% chance that 6 (or any other given number) never turns up; it can be observed that as n increases, the probability of a 1/n-chance event never appearing after n tries rapidly converges to 0.
In probability theory, odds provide a measure of the probability of a particular outcome. Odds are commonly used in gambling and statistics.For example for an event that is 40% probable, one could say that the odds are "2 in 5", "2 to 3 in favor", or "3 to 2 against".
E.g. £100 each-way fivefold accumulator with winners at Evens ( 1 ⁄ 4 odds a place), 11-8 ( 1 ⁄ 5 odds), 5-4 ( 1 ⁄ 4 odds), 1-2 (all up to win) and 3-1 ( 1 ⁄ 5 odds); total staked = £200 Note: 'All up to win' means there are insufficient participants in the event for place odds to be given (e.g. 4 or fewer runners in a horse race).
For example, one has to buy 13,983,816 different tickets to ensure to win the jackpot in a 6/49 game. Lottery organizations have laws, rules and safeguards in place to prevent gamblers from executing such an operation. Further, just winning the jackpot by buying every possible combination does not guarantee that one will break even or make a ...
An odds ratio (OR) is a statistic that quantifies the strength of the association between two events, A and B. The odds ratio is defined as the ratio of the odds of event A taking place in the presence of B, and the odds of A in the absence of B. Due to symmetry, odds ratio reciprocally calculates the ratio of the odds of B occurring in the presence of A, and the odds of B in the absence of A.
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For an event X that occurs with very low probability of 0.0000001%, or once in one billion trials, in any single sample (see also almost never), considering 1,000,000,000 as a "truly large" number of independent samples gives the probability of occurrence of X equal to 1 − 0.999999999 1000000000 ≈ 0.63 = 63% and a number of independent ...