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This book takes full advantage of the latest advances in analog integrated circuits, computer-aided design, electronic publishing, and the World Wide Web's implications for publication support and distribution. Coverage opens with an introduction to the operational amplifier integrated circuit, then presents chapters on amplifiers and feedback; digital control of analog functions; power supplies and ic regulators; operational amplifier characteristics; layout and fabrication of analog circuits; single supply amplifiers; waveform generators; active filters; and nonlinear circuits. For practicing analog integrated circuit designers and anyone interested in applications and design with analog integrated circuits.

Timely, comprehensive, practical--an important working resource for all who use this critical statistical method Discrete Multivariate Distributions is the only comprehensive, single-source reference for this increasingly important statistical subdiscipline. It covers all significant advances that have occurred in the field over the past quarter century in the theory, methodology, computational procedures, and applications of discrete multivariate distributions in a wide range of disciplines. Distributions covered include multinomial, binomial, negative binomial, Poisson, power series, hypergeometric, Plya-Eggenberger, Ewens, orders, and some families of distributions. Each distribution is presented in its own chapter, along with necessary details and descriptions of real-world applications gleaned from the current literature on discrete multivariate distributions. Discrete Multivariate Distributions is the fourth volume of the ongoing revision of Johnson and Kotz's acclaimed Distributions in Statistics--universally acknowledged to be the definitive work on statistical distributions. Originally planned as a revision of Chapter 11 of that classic, this project soon blossomed into a substantial volume as a result of the unprecedented growth that has occurred in the literature on discrete multivariate distributions and their applications over the past quarter century. The only comprehensive, single-volume work on the subject, this valuable reference affords statisticians direct access to all of the latest developments concerning discrete multivariate distributions. Concentrating primarily on areas of interest to theoretical as well as applied statisticians, the authors providecomplete coverage of several important discrete multivariate distributions. These include multinomial, binomial, negative binomial, Poisson, power series, hypergeometric, Plya-Eggenberger, Ewens, orders, and some families of distributions.

Multivariate normal distribution - In probability theory and statistics, a multivariate normal distribution, also sometimes called a multivariate Gaussian distribution, is a specific probability distribution, which can be thought of as a generalization to higher dimensions of the one-dimensional normal distribution (also called a Gaussian distribution).

Cantor distribution - The Cantor distribution is the probability distribution whose cumulative distribution function is the Cantor function. This distribution is not absolutely continuous with respect to Lebesgue measure, so it has no probability density function; neither is it discrete, since it has no point-masses; nor is it even a mixture of a discrete probability distribution with one that has a density function.

Levy skew alpha-stable distribution - In probability theory, a LÃ©vy skew alpha-stable distribution or just stable distribution, developed by Paul LÃ©vy, is a probability distribution where sums of independent identically distributed random variables have the same distribution as the original. If X_1, X_2 are stable and independently and identically distributed (iid) and if Y = aX_1 + bX_2 + c is a linear combination of the two, then Y is of the same type: Y = dX + e.

Multinomial distribution - In probability theory, the multinomial distribution is a generalization of the binomial distribution. The binomial distribution is the probability distribution of the number of "successes" in n independent Bernoulli trials, with the same probability of "success" on each trial.

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