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Titlebook: Statistical Inference and Prediction in Climatology; A Bayesian Approach Edward S. Epstein Book 1985 American Meteorological Society 1985 S

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书目名称Statistical Inference and Prediction in Climatology
副标题A Bayesian Approach
编辑Edward S. Epstein
视频video
概述Written by experts, Peer reviewed, Comprehensive in scope
丛书名称Meteorological Monographs
图书封面Titlebook: Statistical Inference and Prediction in Climatology; A Bayesian Approach Edward S. Epstein Book 1985 American Meteorological Society 1985 S
描述The climatologist (like the hydrologist, the economist, the social scientist, and others) is frequently faces with situations in which a prediction must be made of the outcome of a process that is inherently probabilistic, and this inherent uncertainty is compounded by the expert‘s limited knowledge of the process itself. An example might be predicting next summer‘s mean temperature at a previously unmonitored location. This monograph deals with the balanced use of expert judgment and limited data in such situations. How does the expert quantify his or her judgment? When data are plentiful they can tell a complete story, but how does one alter prior judgment in the light of a few observations, and integrate that information into a consistent and knowledgeable prediction? Bayes theorem provides a straightforward rule for modifying a previously held belief in the light of new data. Bayesian methods are valuable and practical. This monograph is intended to introduce some concepts of statistical inference and prediction that are not generally treated in the traditional college course in statistics, and have not seen their way into the technical literature generally available to the pra
出版日期Book 1985
关键词Statistics; Statistical inference; Forecast techniques; Bernoulli and Poisson processes; fundamentals of
版次1
doihttps://doi.org/10.1007/978-1-935704-27-0
isbn_ebook978-1-935704-27-0Series ISSN 0065-9401
issn_series 0065-9401
copyrightAmerican Meteorological Society 1985
The information of publication is updating

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Normal Linear Regression,hich we shall deal, not only is the distribution of the random variable normal, with mean given by a linear function of the other, predictor variables, but the precision of that normal distribution is taken to be constant, and in particular is not dependent on the values of the predictors.
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Some Fundamentals of Probability,view and reinforcement of important concepts and tools for readers who have studied probability and statistics at the level of an introductory college course that makes use of the calculus. Topics are chosen with the very specific purpose of introducing the tools and methods that will be encountered
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Bernoulli Processes,teristic of a Bernoulli process is that the probability of each of the two mutually exclusive and exhaustive outcomes, say . and — . (not .), is constant from trial to trial. In particular, these probabilities, . and 1 — ., do not depend on how many trials have taken place, the outcomes of other tri
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Normal Data-Generating Processes,plications. The reason for the omnipresence of the normal process lies mostly in the Central Limit Theorem, a very important and powerful theorem of statistics which states that in the limit, as . becomes large, the mean value of a set of . independent realizations from . probability distribution wi
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