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In probability theory, the law of rare events or Poisson limit theorem states that the Poisson distribution may be used as an approximation to the binomial distribution, under certain conditions. [1] The theorem was named after Siméon Denis Poisson (1781–1840). A generalization of this theorem is Le Cam's theorem
In probability theory, Le Cam's theorem, named after Lucien Le Cam, states the following. [1] [2] [3] Suppose: ,,, … are independent random variables, each with a Bernoulli distribution (i.e., equal to either 0 or 1), not necessarily identically distributed.
Cameron–Martin theorem; Campbell's theorem (probability) Central limit theorem; Characterization of probability distributions; Chung–Erdős inequality; Condorcet's jury theorem; Continuous mapping theorem; Contraction principle (large deviations theory) Coupon collector's problem; Cox's theorem; Cramér–Wold theorem; Cramér's theorem ...
Central limit theorem. Central limit theorem (illustration) – redirects to Illustration of the central limit theorem; Central limit theorem for directional statistics; Lyapunov's central limit theorem; Martingale central limit theorem; Central moment; Central tendency; Census; Cepstrum; CHAID – CHi-squared Automatic Interaction Detector
In probability theory and statistics, the Poisson distribution (/ ˈ p w ɑː s ɒ n /; French pronunciation:) is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time if these events occur with a known constant mean rate and independently of the time since the last event. [1]
In probability theory and statistics, a Hawkes process, named after Alan G. Hawkes, is a kind of self-exciting point process. [1] It has arrivals at times < < < < where the infinitesimal probability of an arrival during the time interval [, +) is
Breakfast foods like processed meats, bread, pastries and fried potatoes should be replaced on the breakfast plate instead of good-for-you eggs, says a certified holistic nutritionist. Here's why.
The simplest and most ubiquitous example of a point process is the Poisson point process, which is a spatial generalisation of the Poisson process. A Poisson (counting) process on the line can be characterised by two properties : the number of points (or events) in disjoint intervals are independent and have a Poisson distribution. A Poisson ...