About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 64. Chapters: Probability density function, Cumulative distribution function, Expected value, Kurtosis, Skewness, Central moment, Location parameter, Cumulant, Characteristic function, Distance correlation, Natural exponential family, L-moment, Moment-generating function, Mean difference, Quantile function, Unimodality, Probability-generating function, Compound probability distribution, Truncated distribution, Monotone likelihood ratio, Algebraic statistics, Joint probability distribution, Marginal distribution, Infinite divisibility, Law of total cumulance, Hamburger moment problem, Coefficient of variation, Compound Poisson distribution, List of convolutions of probability distributions, Stochastic ordering, Memorylessness, Scale parameter, Law of total variance, Truncation, Mean-preserving spread, Isserlis' theorem, Law of total expectation, Circular error probable, Stability, Panjer recursion, Limiting density of discrete points, Asymptotic distribution, Concentration parameter, Law of total covariance, Statistical parameter, Stieltjes moment problem, Probability integral transform, Hausdorff moment problem, Probable error, Pairwise independence, Factorial moment generating function, Standardized moment, Subindependence, Smoothness, Shape parameter, Probabilistic metric space, Conditional variance, Ursell function, Probability distribution function. Excerpt: In probability theory and statistics, the cumulants n of a probability distribution are a set of quantities that provide an alternative to the moments of the distribution. The moments determine the cumulants in the sense that any two probability distributions whose moments are identical will have identical cumulants as well, and similarly the cumulants determine the moments. In some cases theoretical treatments of problems in terms of cumulants are simpler than those usi...
