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2. Optimal stopping - Wikipedia

   en.wikipedia.org/wiki/Optimal_stopping

   1.1 Discrete time case. 1.2 Continuous time case. 2 Solution methods. 3 A jump diffusion result. 4 Examples. Toggle Examples subsection. 4.1 Coin tossing. 4.2 House ...

3. Discrete uniform distribution - Wikipedia

   en.wikipedia.org/wiki/Discrete_uniform_distribution

   In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein each of some finite whole number n of outcome values are equally likely to be observed. Thus every one of the n outcome values has equal probability 1/n. Intuitively, a discrete uniform distribution is "a known, finite number ...

4. Probability distribution - Wikipedia

   en.wikipedia.org/wiki/Probability_distribution

   For instance, if X is used to denote the outcome of a coin toss ("the experiment"), then the probability distribution of X would take the value 0.5 (1 in 2 or 1/2) for X = heads, and 0.5 for X = tails (assuming that the coin is fair). More commonly, probability distributions are used to compare the relative occurrence of many different random ...

5. German tank problem - Wikipedia

   en.wikipedia.org/wiki/German_tank_problem

   For example, taking the symmetric 95% interval p = 2.5% and q = 97.5% for k = 5 yields 0.025 1/5 ≈ 0.48, 0.975 1/5 ≈ 0.995, so the confidence interval is approximately [1.005m, 2.08m]. The lower bound is very close to m , thus more informative is the asymmetric confidence interval from p = 5% to 100%; for k = 5 this yields 0.05 1/5 ≈ 0.55 ...

6. Category:Discrete distributions - Wikipedia

   en.wikipedia.org/wiki/Category:Discrete...

   Pages in category "Discrete distributions" The following 51 pages are in this category, out of 51 total. ... Maximum entropy probability distribution; Mixed Poisson ...

7. Hypergeometric distribution - Wikipedia

   en.wikipedia.org/wiki/Hypergeometric_distribution

   In probability theory and statistics, the hypergeometric distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws, without replacement, from a finite population of size that contains exactly objects with that feature, wherein each draw is either a success or a failure.