Bayesian Forecasting and Dynamic ModelsSpringer Science & Business Media, 29 de jun. de 2013 - 704 páginas In this book we are concerned with Bayesian learning and forecast ing in dynamic environments. We describe the structure and theory of classes of dynamic models, and their uses in Bayesian forecasting. The principles, models and methods of Bayesian forecasting have been developed extensively during the last twenty years. This devel opment has involved thorough investigation of mathematical and sta tistical aspects of forecasting models and related techniques. With this has come experience with application in a variety of areas in commercial and industrial, scientific and socio-economic fields. In deed much of the technical development has been driven by the needs of forecasting practitioners. As a result, there now exists a relatively complete statistical and mathematical framework, although much of this is either not properly documented or not easily accessible. Our primary goals in writing this book have been to present our view of this approach to modelling and forecasting, and to provide a rea sonably complete text for advanced university students and research workers. The text is primarily intended for advanced undergraduate and postgraduate students in statistics and mathematics. In line with this objective we present thorough discussion of mathematical and statistical features of Bayesian analyses of dynamic models, with illustrations, examples and exercises in each Chapter. |
Conteúdo
CHAPTER 10 | 319 |
88 | 321 |
CHAPTER 11 | 383 |
CHAPTER 12 | 439 |
of analysis | 456 |
CHAPTER 13 | 512 |
CHAPTER 14 | 547 |
CHAPTER 15 | 584 |
CHAPTER 6 | 174 |
CHAPTER 7 | 202 |
82 | 211 |
CHAPTER 8 | 231 |
86 | 262 |
CHAPTER 9 | 274 |
MULTIVARIATE MODELLING AND FORECASTING | 596 |
477 | 633 |
CHAPTER 16 | 653 |
677 | |
691 | |
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Termos e frases comuns
A₁ analysis apply approximation assumed Bayes Bayesian calculated changes Chapter coefficients components conjugate prior Consider constant covariance defined Definition density discount factors dynamic eigenvalues estimate evolution equation evolution error evolution variance example Figure filtered first-order polynomial follows forecast distribution forecast errors forecast function ft(k gamma distribution given grid harmonic Harrison initial prior integration intervention limiting linear linear models M₁ mean and variance mean response multi-process multivariate noise non-linear non-zero observational error observational variance one-step forecast parameters point forecasts polynomial polynomial model posterior distributions posterior probability provides quadratic growth quantities regression regressor representation seasonal effects seasonal factors Section sequence similarity matrix simply structure Suppose Theorem tion trend TSDLM univariate updating equations usual values variables variance matrix variation vector W₁ Y₁ Yt series zero