標(biāo)題: Titlebook: Bayesian Forecasting and Dynamic Models; Mike West,Jeff Harrison Book 19891st edition Springer Science+Business Media New York 1989 data a [打印本頁] 作者: 粗野的整個 時間: 2025-3-21 19:37
書目名稱Bayesian Forecasting and Dynamic Models影響因子(影響力)
書目名稱Bayesian Forecasting and Dynamic Models影響因子(影響力)學(xué)科排名
書目名稱Bayesian Forecasting and Dynamic Models網(wǎng)絡(luò)公開度
書目名稱Bayesian Forecasting and Dynamic Models網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Bayesian Forecasting and Dynamic Models被引頻次
書目名稱Bayesian Forecasting and Dynamic Models被引頻次學(xué)科排名
書目名稱Bayesian Forecasting and Dynamic Models年度引用
書目名稱Bayesian Forecasting and Dynamic Models年度引用學(xué)科排名
書目名稱Bayesian Forecasting and Dynamic Models讀者反饋
書目名稱Bayesian Forecasting and Dynamic Models讀者反饋學(xué)科排名
作者: 抓住他投降 時間: 2025-3-21 23:39
Liang Lin,Dongyu Zhang,Ping Luo,Wangmeng Zuore the second-order models, also sometimes referred to as linear growth models, that much of this chapter is concerned with. Higher order polynomial models are also discussed for completeness, although it is rare that polynomials of order greater than three are required for practice. The structure o作者: expdient 時間: 2025-3-22 02:18 作者: 腐敗 時間: 2025-3-22 06:34
Euzebiusz Jamrozik,Michael J. Selgelidl components are a useful first attack. If retrospective analysis is the primary goal, then these simple and purely descriptive models may be adequate in themselves, providing estimates of the trend (or deseasonalised series), seasonal pattern (detrended series) and irregular or random component ove作者: chiropractor 時間: 2025-3-22 09:08
Jacques Berleur,Tanguy Ewbank de Wespined models in Chapter 10 where deterioration in forecasting performance, though small, is apparent. In this Chapter, we move closer to illustrating forecasting systems rather than simply models, considering ways in which routine interventions can be incorporated into existing DLMs, and examples of wh作者: Recessive 時間: 2025-3-22 15:14 作者: urethritis 時間: 2025-3-22 18:47 作者: creditor 時間: 2025-3-23 00:15 作者: RAGE 時間: 2025-3-23 03:26
Book 19891st edition 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 mat作者: 詢問 時間: 2025-3-23 06:33 作者: Cabinet 時間: 2025-3-23 10:22 作者: Climate 時間: 2025-3-23 14:11 作者: 桶去微染 時間: 2025-3-23 20:38
Illustrations and Extensions of Standard DLMS,l components are a useful first attack. If retrospective analysis is the primary goal, then these simple and purely descriptive models may be adequate in themselves, providing estimates of the trend (or deseasonalised series), seasonal pattern (detrended series) and irregular or random component ove作者: Yourself 時間: 2025-3-24 01:43 作者: plasma-cells 時間: 2025-3-24 03:45 作者: cultivated 時間: 2025-3-24 09:47
Multivariate Modelling and Forecasting,al distribution, the univariate theory extends easily only when it is assumed that the observational error variance matrices are known for all time. However, as soon as uncertainties about observational variance matrices are admitted, the tractability of analysis is lost. In general, there is no nea作者: voluble 時間: 2025-3-24 14:17 作者: Inflammation 時間: 2025-3-24 15:31 作者: CON 時間: 2025-3-24 22:36
Johanna Sefyrin,Mariana S. Gustafssonn-normal problems, the largest class being that based on the use of . for observational distributions. This Chapter is devoted to these models, the primary references being Migon (1984), Migon and Harrison (1985), and West and Harrison (1986a), West, Harrison and Migon (1985).作者: 生命層 時間: 2025-3-25 02:07 作者: 散步 時間: 2025-3-25 06:15
Exponential Family Dynamic Models,n-normal problems, the largest class being that based on the use of . for observational distributions. This Chapter is devoted to these models, the primary references being Migon (1984), Migon and Harrison (1985), and West and Harrison (1986a), West, Harrison and Migon (1985).作者: 作繭自縛 時間: 2025-3-25 10:33 作者: intellect 時間: 2025-3-25 15:36 作者: Coronary 時間: 2025-3-25 18:40 作者: 晚來的提名 時間: 2025-3-25 23:17
Liang Lin,Dongyu Zhang,Ping Luo,Wangmeng Zuod from the various TSDLMs of the previous chapter. Together with complementary forms from models for the effects of independent variables, these provide essentially all practically important dynamic linear models.作者: 煩人 時間: 2025-3-26 00:34 作者: 門閂 時間: 2025-3-26 07:38
Model Specification and Design,d from the various TSDLMs of the previous chapter. Together with complementary forms from models for the effects of independent variables, these provide essentially all practically important dynamic linear models.作者: 革新 時間: 2025-3-26 09:23
Appendix: Distribution Theory and Linear Algebra,re discussed in detail as they are of some importance and should be clearly understood. For a comprehensive reference to all the distribution theory, see Johnson and Kotz (1972). For distribution theory specifically within the contexts of Bayesian analyses, see Aitchison and Dunsmore (1976), Box and Tiao (1973), and De Groot (1971).作者: PHON 時間: 2025-3-26 14:54 作者: 砍伐 時間: 2025-3-26 20:28 作者: 遺留之物 時間: 2025-3-27 00:24
Univariate Time Series DLM Theory,The class of univariate Time Series DLMs, denoted TSDLMs, was introduced in Section 4.2 as defined by quadruples of the form.for any .. and ... When this is understood, we refer to the quadruple simply as作者: 儲備 時間: 2025-3-27 04:26 作者: floaters 時間: 2025-3-27 06:21 作者: homocysteine 時間: 2025-3-27 11:33 作者: Choreography 時間: 2025-3-27 16:06 作者: intricacy 時間: 2025-3-27 19:51
Springer Science+Business Media New York 1989作者: compose 時間: 2025-3-28 00:59 作者: Haphazard 時間: 2025-3-28 05:11
Smart Innovation, Systems and Technologiesary). More broadly, a model is any scheme of description and explanation that organises information and experiences providing a means of learning and forecasting. The prime reason for modelling is to provide efficient learning processes which will enhance understanding and enable wise decisions.作者: 含糊 時間: 2025-3-28 07:10 作者: Entreaty 時間: 2025-3-28 12:02 作者: Estrogen 時間: 2025-3-28 15:18
Smart Innovation, Systems and Technologiesary). More broadly, a model is any scheme of description and explanation that organises information and experiences providing a means of learning and forecasting. The prime reason for modelling is to provide efficient learning processes which will enhance understanding and enable wise decisions.作者: morale 時間: 2025-3-28 18:43
Smart Innovation, Systems and Technologiesway of introduction to DLMs, this case is described and examined in detail in this Chapter. The first-order polynomial model is the simple, yet non-trivial, time series model in which the observation series .. is represented as .. = μ. + ν., μ. being the current . of the series at time ., and ν. ～ N作者: Affluence 時間: 2025-3-28 23:26 作者: 小樣他閑聊 時間: 2025-3-29 05:57 作者: Humble 時間: 2025-3-29 09:44
Liang Lin,Dongyu Zhang,Ping Luo,Wangmeng Zuofundamental interest in model design. The expectation of this, the forecast function ..(.), provides the forecaster’s view of the expected development of the series and we focus on this, rather than the mean response itself, as the central guide to constructing appropriate models. This is purely con作者: 拱墻 時間: 2025-3-29 15:15
Liang Lin,Dongyu Zhang,Ping Luo,Wangmeng Zuoimental design. Their use in time series modelling is to provide simple yet flexible forms to describe the local trend components, where by trend we mean smooth variation in time. Relative to the sampling interval of the series and the required forecast horizons, such trends can frequently be well a作者: 濕潤 時間: 2025-3-29 18:53 作者: 協(xié)迫 時間: 2025-3-29 20:43
The Foundation and Advances of Deep Learningon regression relationships; the three classes combined via the principle of superposition provide for the vast majority of forms of behaviour encountered in practice for which linear models can provide adequate descriptions. Here we describe the theory and analysis of regression DLMs and important 作者: Enthralling 時間: 2025-3-30 02:52
Euzebiusz Jamrozik,Michael J. Selgelid. Here we switch the focus to the latter to consolidate what has been developed in theory, illustrating many basic concepts via analyses of typical datasets. We consider both retrospective analysis of a time series as well as forecasting with an existing model, and describe a variety of modelling ac作者: synovitis 時間: 2025-3-30 06:00 作者: PAGAN 時間: 2025-3-30 12:01 作者: SLING 時間: 2025-3-30 14:46
Some Great Myths of the History of ComputingMs. Although the full class of DLMs provides an enormous variety of useful models, it is the case that, sometimes, elaborations to include models with . parameters result in such non-linearities, thus requiring extensions of the usual linear model analysis. Some typical, and important, examples are
