標(biāo)題: Titlebook: Likelihood and Bayesian Inference; With Applications in Leonhard Held,Daniel Sabanés Bové Textbook 2020Latest edition Springer-Verlag GmbH [打印本頁] 作者: protocol 時間: 2025-3-21 16:26
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作者: ADORE 時間: 2025-3-21 21:34 作者: 主講人 時間: 2025-3-22 03:50
Textbook 2020Latest editionimportance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wal作者: 花束 時間: 2025-3-22 08:03
1431-8776 medicine and epidemiology with programming examples in the This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of 作者: abject 時間: 2025-3-22 10:40
Likelihood,tion, and Fisher information. Computational algorithms are treated to compute the maximum likelihood estimate, such as optimisation and the EM algorithm. The concept of sufficiency and the likelihood principle are finally discussed in some detail. Exercises are given at the end.作者: Visual-Acuity 時間: 2025-3-22 15:10
Frequentist Properties of the Likelihood,the corresponding confidence intervals are introduced. Variance-stabilising transformations are also discussed. A case study comparing coverage and width of several confidence intervals for a proportion finishes this chapter, completed by a number of exercises at the end.作者: Tracheotomy 時間: 2025-3-22 20:48 作者: 使尷尬 時間: 2025-3-22 21:59
Model Selection,its connection to cross-validation. Bayesian model selection based on the marginal likelihood is described, including Bayesian model averaging. Finally, DIC is introduced, completed by a number of exercises at the end.作者: 競選運動 時間: 2025-3-23 03:09
Numerical Methods for Bayesian Inference, provide ways to numerically compute posterior characteristics of interest. Monte Carlo methods, including Monte Carlo integration, rejection and importance sampling as well as Markov chain Monte Carlo are described. Finally, numerical computation of the marginal likelihood, necessary for Bayesian m作者: 殺人 時間: 2025-3-23 05:46
Prediction, predictions, obtained with either a likelihood or Bayesian approach. Connections to the simpler plug-in prediction are also described. Finally, methods to assess the quality of probabilistic predictions, such as the Brier and the logarithmic score, are described. Exercises are given at the end.作者: ascetic 時間: 2025-3-23 10:36 作者: 構(gòu)想 時間: 2025-3-23 17:57 作者: 指派 時間: 2025-3-23 20:44 作者: hypnogram 時間: 2025-3-24 00:09
Leonhard Held,Daniel Sabanés Bovéhes.Conquor Industry 4.0 problems and solutionsRapidly prototype and program new IoT and Edge solutions using low-cost Maker tech, such as those from Arduino, Raspberry Pi and Nvidia. With a focus on the electronics, this book allows experienced computer science students as well as researchers, prac作者: 矛盾心理 時間: 2025-3-24 04:56
Leonhard Held,Daniel Sabanés Bovéhes.Conquor Industry 4.0 problems and solutionsRapidly prototype and program new IoT and Edge solutions using low-cost Maker tech, such as those from Arduino, Raspberry Pi and Nvidia. With a focus on the electronics, this book allows experienced computer science students as well as researchers, prac作者: foppish 時間: 2025-3-24 06:48 作者: amputation 時間: 2025-3-24 11:36
Leonhard Held,Daniel Sabanés Bovélly decentralized access control architecture for the IoT.PrThis book presents the design and development of an access control architecture for the Internet of Things (IoT) systems. It considers the significant authentication and authorization issues for large-scale IoT systems, in particular, the n作者: creditor 時間: 2025-3-24 16:41 作者: NOVA 時間: 2025-3-24 19:49
Leonhard Held,Daniel Sabanés Bovéore complex systems. In agri-food industrial systems, an artificial intelligent system would drive a system towards the set objectives based on the information and knowledge gathered from the consumers, farmers, machines, and domain experts. Machine learning (ML) is the preferred tool to process the作者: SPALL 時間: 2025-3-24 23:34 作者: 職業(yè) 時間: 2025-3-25 03:21
Leonhard Held,Daniel Sabanés Bovéan inevitable platform in all engineering domains. The need for sophisticated and ambient environments controlled by tech has resulted in an exponential growth of automation and artificial intellig978-1-4842-8107-9978-1-4842-8108-6Series ISSN 2948-2542 Series E-ISSN 2948-2550 作者: 討厭 時間: 2025-3-25 11:01 作者: 退出可食用 時間: 2025-3-25 13:23
1431-8776 ce” has been expanded to include new material on?Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical 978-3-662-60794-7978-3-662-60792-3Series ISSN 1431-8776 Series E-ISSN 2197-5671 作者: septicemia 時間: 2025-3-25 17:10
Leonhard Held,Daniel Sabanés Bové healthcare integration components. The chapter presents seamless applications dispensed by CloudIoT platform and contemplates discussion on factors driving CloudIoT health integration. The chapter also presents a conceptual architectural framework for healthcare monitoring system that considers a r作者: 燦爛 時間: 2025-3-25 23:25
Leonhard Held,Daniel Sabanés Bovéance and enhance their feature. Healthcare assistance supports the continual therapy, observance of victims at intervals the unit, and offers around the timekeeper help and makes sure that the encircling setting does not hinder their treatment and observance. Recently, an IoT (Internet of Things) po作者: 頌揚國家 時間: 2025-3-26 01:39 作者: BUOY 時間: 2025-3-26 05:09
Leonhard Held,Daniel Sabanés Bovéthe efficiency of decision-making by analyzing harvest statistics. Further, it explores climate-smart methods, known as smart agriculture, that have been adopted by a number of Indian farmers..978-981-15-0665-9978-981-15-0663-5Series ISSN 2197-6503 Series E-ISSN 2197-6511 作者: Arroyo 時間: 2025-3-26 09:09
Leonhard Held,Daniel Sabanés Bové deep learning with the emphasis on their application for AI implementation in the field of agri-food material handling. Popular ML algorithms viz., support vector machine (SVM), K-nearest neighbor (KNN), artificial neural networks (ANN), decision trees, and convolutional neural networks (CNN) are d作者: FOIL 時間: 2025-3-26 15:27 作者: Crumple 時間: 2025-3-26 19:11
Textbook 2020Latest editiont and Bayesian perspectives. This revised edition of the book “Applied Statistical Inference” has been expanded to include new material on?Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical 作者: Genome 時間: 2025-3-26 21:40
Leonhard Held,Daniel Sabanés Bovéncertainty that exists in such systems. Fully explaining all the techniques used, the book is of interest to engineers, researchers and scientists working in the field of the wireless sensor networks, IoT systems and their access control management. ?978-3-030-65000-1978-3-030-64998-2Series ISSN 2194-8402 Series E-ISSN 2194-8410 作者: gerrymander 時間: 2025-3-27 01:50 作者: Aspirin 時間: 2025-3-27 07:58 作者: Consensus 時間: 2025-3-27 10:43 作者: 似少年 時間: 2025-3-27 14:28
Likelihood Inference in Multiparameter Models,The concepts described in Chap. . are now extended to multiparameter models. The concept of profile likelihood is introduced as well as the generalised likelihood ratio statistic. The conditional likelihood, an alternative way to eliminate a nuisance parameter, is discussed. Exercises are given at the end.作者: ALE 時間: 2025-3-27 19:37
978-3-662-60794-7Springer-Verlag GmbH Germany, part of Springer Nature 2020作者: 鄙視讀作 時間: 2025-3-27 22:28
Likelihood and Bayesian Inference978-3-662-60792-3Series ISSN 1431-8776 Series E-ISSN 2197-5671 作者: Panacea 時間: 2025-3-28 02:44 作者: 我不重要 時間: 2025-3-28 08:37 作者: 粗俗人 時間: 2025-3-28 13:25
Likelihood,tion, and Fisher information. Computational algorithms are treated to compute the maximum likelihood estimate, such as optimisation and the EM algorithm. The concept of sufficiency and the likelihood principle are finally discussed in some detail. Exercises are given at the end.作者: 新陳代謝 時間: 2025-3-28 14:39
Frequentist Properties of the Likelihood,the corresponding confidence intervals are introduced. Variance-stabilising transformations are also discussed. A case study comparing coverage and width of several confidence intervals for a proportion finishes this chapter, completed by a number of exercises at the end.作者: Explosive 時間: 2025-3-28 21:33
Bayesian Inference,ian point and interval estimates. Bayesian inference in multiparameter models is discussed and some results from Bayesian asymptotics are described. Finally, empirical Bayes methods are described, completed by a number of exercises at the end.作者: 傾聽 時間: 2025-3-29 00:53
Model Selection,its connection to cross-validation. Bayesian model selection based on the marginal likelihood is described, including Bayesian model averaging. Finally, DIC is introduced, completed by a number of exercises at the end.作者: FLING 時間: 2025-3-29 06:17
Prediction, predictions, obtained with either a likelihood or Bayesian approach. Connections to the simpler plug-in prediction are also described. Finally, methods to assess the quality of probabilistic predictions, such as the Brier and the logarithmic score, are described. Exercises are given at the end.作者: 業(yè)余愛好者 時間: 2025-3-29 08:12
Overview,ructures, and the intention of this book. Energetic materials are a special group of energy materials applied for both civilian and military purposes, due to their high efficiency of gas and heat release. The intrinsic structure of an energetic material refers to crystal packing and the substructure
