標(biāo)題: Titlebook: Bayesian Spatial Modelling with Conjugate Prior Models; Henning Omre,Torstein M. Fjeldstad,Ole Bernhard Fo Textbook 2024 The Editor(s) (if [打印本頁(yè)] 作者: EXERT 時(shí)間: 2025-3-21 19:11
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書目名稱Bayesian Spatial Modelling with Conjugate Prior Models讀者反饋學(xué)科排名
作者: Shuttle 時(shí)間: 2025-3-21 21:55 作者: 鳴叫 時(shí)間: 2025-3-22 03:41 作者: debase 時(shí)間: 2025-3-22 05:16 作者: ZEST 時(shí)間: 2025-3-22 10:58
Metaheuristics for Vehicle Routing Problems,saic variables, respectively. For each of these prior models, detailed discussions of their parametrisations and characteristics are presented. We discuss topics specifically related to each prior model and present prior models based on hierarchical versions of the random fields under study. The running examples are continued.作者: dry-eye 時(shí)間: 2025-3-22 13:37
Lecture Notes in Computer Science book. This exposure facilitates the understanding of the basic concepts and the ability to compare them. Topics discussed include classes of simulation algorithms, geostatistical models, Gaussian Markov random fields, basis function models, functional predictors and the integrated nested Laplace approximations.作者: Nuance 時(shí)間: 2025-3-22 19:37
https://doi.org/10.1007/978-3-031-65418-3Spatial Statistics; Bayesian Modelling; Conjugate Bayesian Models; Spatial Simulation and Prediction; Sp作者: Detain 時(shí)間: 2025-3-22 21:16 作者: exclamation 時(shí)間: 2025-3-23 04:20 作者: 大暴雨 時(shí)間: 2025-3-23 09:32
http://image.papertrans.cn/b/image/192628.jpg作者: debunk 時(shí)間: 2025-3-23 13:01
Günther Z?pfel,Roland Braune,Michael B?glThis chapter contains a brief description of the characteristics of probability and statistics. Combined with observations from the phenomenon under study, these characteristics may be used in a descriptive, predictive or confirmatory setting. The book is written with a predictive statistical focus.作者: 舊式步槍 時(shí)間: 2025-3-23 16:48
https://doi.org/10.1007/978-3-031-62912-9This chapter contains suitable project texts and exercises, sorted by spatial variable type. The datasets used in the projects, along with the suggested solutions to the exercises, can be downloaded from the book’s homepage.作者: 營(yíng)養(yǎng) 時(shí)間: 2025-3-23 18:58
Introduction,This chapter contains a brief description of the characteristics of probability and statistics. Combined with observations from the phenomenon under study, these characteristics may be used in a descriptive, predictive or confirmatory setting. The book is written with a predictive statistical focus.作者: 生氣的邊緣 時(shí)間: 2025-3-23 22:37
Projects and Exercises,This chapter contains suitable project texts and exercises, sorted by spatial variable type. The datasets used in the projects, along with the suggested solutions to the exercises, can be downloaded from the book’s homepage.作者: Accommodation 時(shí)間: 2025-3-24 04:29 作者: 單挑 時(shí)間: 2025-3-24 08:55 作者: 大方不好 時(shí)間: 2025-3-24 11:51 作者: 斥責(zé) 時(shí)間: 2025-3-24 16:45 作者: LEERY 時(shí)間: 2025-3-24 22:58 作者: Concomitant 時(shí)間: 2025-3-24 23:34 作者: LAST 時(shí)間: 2025-3-25 06:32 作者: MURAL 時(shí)間: 2025-3-25 07:37
A Continuous-GRASP Random-Key Optimizergested whenever these requirements cannot be met. The primary computational challenge is faced in large studies with a large number of observations. Assessment of the posterior model based on brute-force McMC approaches is unfeasible in such studies. Models such as the Kriging predictor, the Gaussia作者: 任意 時(shí)間: 2025-3-25 13:59
Lecture Notes in Computer Science book. This exposure facilitates the understanding of the basic concepts and the ability to compare them. Topics discussed include classes of simulation algorithms, geostatistical models, Gaussian Markov random fields, basis function models, functional predictors and the integrated nested Laplace ap作者: 整頓 時(shí)間: 2025-3-25 18:46
Ethan Gibbons,Beatrice Ombuki-Bermanmethodologies and applications. One absolute requirement is that real data are involved in the study. The applied publications reflect the modelling of spatial continuous, event and mosaic variables and a mixture of them. The applications span epidemiology, basketball shooting, sub-surface geology a作者: MEET 時(shí)間: 2025-3-25 22:42
of observation likelihood and phenomenon prior spatial model.This book offers a comprehensive overview of statistical methodology for modelling and evaluating spatial variables useful in a variety of applications. These spatial variables fall into three categories: continuous, like terrain elevation作者: Acetaminophen 時(shí)間: 2025-3-26 02:54
https://doi.org/10.1007/978-981-15-7571-6l tractability of the ultimate Bayesian solution, namely, the posterior model. Based on this posterior model, spatial prediction with associated quantifications of uncertainty can be obtained. Three simple instructive examples of the conjugate characteristics are presented.作者: grenade 時(shí)間: 2025-3-26 04:49
Hasmat Malik,Atif Iqbal,Farhad Ilahi Bakhshod models representing frequently used observation acquisition procedures for spatial variables. The model parameters may also be specified as random, which results in a hierarchical random field model. The conjugate characteristic is maintained if suitable prior pdfs for these parameters are assigned.作者: DALLY 時(shí)間: 2025-3-26 09:55 作者: ZEST 時(shí)間: 2025-3-26 13:37 作者: Carcinoma 時(shí)間: 2025-3-26 19:20
Random Field Models,od models representing frequently used observation acquisition procedures for spatial variables. The model parameters may also be specified as random, which results in a hierarchical random field model. The conjugate characteristic is maintained if suitable prior pdfs for these parameters are assigned.作者: FEMUR 時(shí)間: 2025-3-26 23:46
Selected Applications,f spatial continuous, event and mosaic variables and a mixture of them. The applications span epidemiology, basketball shooting, sub-surface geology and medical imaging. To appreciate the results of the studies, the reader must read the publication themselves.作者: 類人猿 時(shí)間: 2025-3-27 04:44 作者: 推遲 時(shí)間: 2025-3-27 08:32
Computational Challenges,n Markov model, the basis function model, the kernel predictor and the integrated nested Laplace approximation are presented in the notation of the book. The focus is on their computational efficiency. Approximations are recommended for large studies with a large set of observations.作者: 辯論 時(shí)間: 2025-3-27 10:52 作者: GIDDY 時(shí)間: 2025-3-27 14:11 作者: Ondines-curse 時(shí)間: 2025-3-27 20:17 作者: set598 時(shí)間: 2025-3-28 01:48
Hasmat Malik,Atif Iqbal,Farhad Ilahi Bakhshthree types: continuous, event and mosaic. Motivating examples of modelling each of these spatial variable types are given. Simulation algorithms suitable for assessing this posterior model are presented in detail. Lastly, the notation in the book is established.作者: Afflict 時(shí)間: 2025-3-28 05:58
Marrian H. Gebreselassie,Micheal Olusanyain a hierarchical modelling framework. The posterior pdfs for the most influential parameters can be analytically assessed because conjugate prior pdfs are assigned to them. Thus, estimates with credibility regions are available. The running examples are continued.作者: Pantry 時(shí)間: 2025-3-28 08:06 作者: neolith 時(shí)間: 2025-3-28 14:20 作者: Popcorn 時(shí)間: 2025-3-28 14:43
Textbook 2024s uniquely define the posterior spatial model, which provides the basis for spatial simulations, spatial predictions with associated precisions, and model parameter inference. The emphasis is on Bayesian spatial modelling with conjugate pairs of likelihood and prior models that are analytically trac作者: Concrete 時(shí)間: 2025-3-28 20:52
Bayesian Spatial Modelling with Conjugate Prior Models作者: 繞著哥哥問(wèn) 時(shí)間: 2025-3-29 02:38
Bayesian Spatial Modelling with Conjugate Prior Models978-3-031-65418-3作者: 性別 時(shí)間: 2025-3-29 03:07 作者: observatory 時(shí)間: 2025-3-29 07:34
Bayesian Spatial Modelling,and complex observation acquisition procedures are merely some of these characteristics. Bayes’ rule is presented as the fundamental principle for spatial modelling. The likelihood model represents the observation collection design, whereas the prior model represents expert knowledge and experience.作者: 合并 時(shí)間: 2025-3-29 11:39 作者: 悅耳 時(shí)間: 2025-3-29 18:11
Random Field Models,se respective classes are the Gaussian, Poisson and Markov classes. Prior models from these classes have conjugate properties with respect to likelihood models representing frequently used observation acquisition procedures for spatial variables. The model parameters may also be specified as random,作者: indignant 時(shí)間: 2025-3-29 23:48
