Statistical Inference and Prediction in Climatology: A Bayesian Approach
E. S. Epstein
jun 2016. · Meteorological Monographs 20. knjiga · Springer
E-knjiga
203
Broj stranica
reportOcjene i recenzije nisu potvrđene Saznajte više
O ovoj e-knjizi
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 practising climatologist. Even today, where Bayesian methods are presented the practical aspects of their application are seldom emphasized. Using examples drawn from climatology and meteorology covering probabilistic processes ranging from Bernoulli to normal to autoregression, methods for quantifying beliefs as concise probability statements are described, and the implications of new data on beliefs and of beliefs on predictions are developed.istical 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 practising climatologist. Even today, where Bayesian methods are presented the practical aspects of their application are seldom emphasized. Using examples drawn from climatology and meteorology covering probabilistic processes ranging from Bernoulli to normal to autoregression, methods for quantifying beliefs as concise probability statements are described, and the implications of new data on beliefs and of beliefs on predictions are developed.
Ocijenite ovu e-knjigu
Recite nam šta mislite.
Informacije o čitanju
Pametni telefoni i tableti
Instalirajte aplikaciju Google Play Knjige za Android i iPad/iPhone uređaje. Aplikacija se automatski sinhronizira s vašim računom i omogućava vam čitanje na mreži ili van nje gdje god da se nalazite.
Laptopi i računari
Audio knjige koje su kupljene na Google Playu možete slušati pomoću web preglednika na vašem računaru.
Elektronički čitači i ostali uređaji
Da čitate na e-ink uređajima kao što su Kobo e-čitači, morat ćete preuzeti fajl i prenijeti ga na uređaj. Pratite detaljne upute Centra za pomoć da prenesete fajlove na podržane e-čitače.
