標(biāo)題: Titlebook: Geometric Modeling in Probability and Statistics; Ovidiu Calin,Constantin Udri?te Textbook 2014 Springer International Publishing Switzerl [打印本頁] 作者: 閃爍 時間: 2025-3-21 18:44
書目名稱Geometric Modeling in Probability and Statistics影響因子(影響力)
書目名稱Geometric Modeling in Probability and Statistics影響因子(影響力)學(xué)科排名
書目名稱Geometric Modeling in Probability and Statistics網(wǎng)絡(luò)公開度
書目名稱Geometric Modeling in Probability and Statistics網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Geometric Modeling in Probability and Statistics被引頻次
書目名稱Geometric Modeling in Probability and Statistics被引頻次學(xué)科排名
書目名稱Geometric Modeling in Probability and Statistics年度引用
書目名稱Geometric Modeling in Probability and Statistics年度引用學(xué)科排名
書目名稱Geometric Modeling in Probability and Statistics讀者反饋
書目名稱Geometric Modeling in Probability and Statistics讀者反饋學(xué)科排名
作者: probate 時間: 2025-3-21 22:17
Textbook 2014accompanied by software that is able to. .provide numerical computations of several information geometric objects. The reader will understand a flourishing field of mathematics in which very few books have been written so far..作者: Optic-Disk 時間: 2025-3-22 02:33
https://doi.org/10.1007/978-3-476-03356-7ack can be corrected by introducing another concept, which measures the relative entropy between two given densities. This chapter studies the Kullback–Leibler relative entropy (known also as the Kullback–Leibler divergence) between two probability densities in both discrete and continuous cases.作者: 遠(yuǎn)足 時間: 2025-3-22 05:54
https://doi.org/10.1007/978-3-663-04759-9of mean, variance, or any . moments. The solution of these variational problems belongs to the exponential family. However, explicit solutions exist only in a few particular cases. A distinguished role is played by the study of the Maxwell–Boltzmann distribution.作者: BOGUS 時間: 2025-3-22 10:15
Métabolisme et fonctions rénalestiable manifolds, tangent space, vector fields, differentiable maps, 1-forms, tensors, linear connections, Riemannian manifolds, and the Levi–Civita connection. The material of this chapter forms the basis for next chapters.作者: 新義 時間: 2025-3-22 14:39 作者: 新義 時間: 2025-3-22 18:54 作者: overrule 時間: 2025-3-23 00:50 作者: 時間等 時間: 2025-3-23 05:24
https://doi.org/10.1007/978-3-031-69698-5 the first and second fundamental forms, curvatures, mean curvatures, and the relations among them..This material adapts the well-known theory of submanifolds to the statistical manifolds framework and consists mainly in the contributions of the authors.作者: insert 時間: 2025-3-23 08:38
Kullback–Leibler Relative Entropyack can be corrected by introducing another concept, which measures the relative entropy between two given densities. This chapter studies the Kullback–Leibler relative entropy (known also as the Kullback–Leibler divergence) between two probability densities in both discrete and continuous cases.作者: Nucleate 時間: 2025-3-23 12:07
Maximum Entropy Distributionsof mean, variance, or any . moments. The solution of these variational problems belongs to the exponential family. However, explicit solutions exist only in a few particular cases. A distinguished role is played by the study of the Maxwell–Boltzmann distribution.作者: 不在灌木叢中 時間: 2025-3-23 16:31 作者: 智力高 時間: 2025-3-23 19:05 作者: 上腭 時間: 2025-3-24 01:29 作者: Proponent 時間: 2025-3-24 02:30
Contrast Functions on Statistical Modelsative entropy, .-divergence, Hellinger distance, Chernoff information, Jefferey distance, Kagan divergence, and exponential contrast function. The relation with the skewness tensor and .-connection is made. The goal of this chapter is to produce hands-on examples for the theoretical concepts introduced in Chap. ..作者: critic 時間: 2025-3-24 08:46
Statistical Submanifolds the first and second fundamental forms, curvatures, mean curvatures, and the relations among them..This material adapts the well-known theory of submanifolds to the statistical manifolds framework and consists mainly in the contributions of the authors.作者: 莊嚴(yán) 時間: 2025-3-24 13:17 作者: 向前變橢圓 時間: 2025-3-24 18:10
Dystopische Welten in der ,-TrilogieEntropy is a notion taken form Thermodynamics, where it describes the uncertainty in the movement of gas particles. In this chapter the entropy will be considered as a measure of uncertainty of a random variable.作者: 繁重 時間: 2025-3-24 21:02
https://doi.org/10.1007/978-3-322-88458-9The informational energy is a concept inspired from the kinetic energy expressionof Classical Mechanics. From the information theory point of view, the . is a measure of uncertainty or randomness of a probability system, and was introduced and studied for the first time by Onicescu [67, 68] in the mid-1960s.作者: Dawdle 時間: 2025-3-25 00:44
Explicit ExamplesThis chapter presents a few examples of usual statistical models (normal, lognormal, beta, gamma, Bernoulli, and geometric) for which we provide the Fisher metricexplicitly and, if possible, the geodesicsand .-autoparallelcurves. Some Fisher metrics will involve the use of non-elementary functions, such as the digamma and trigamma functions. 作者: 具體 時間: 2025-3-25 05:34 作者: nominal 時間: 2025-3-25 08:02
Informational EnergyThe informational energy is a concept inspired from the kinetic energy expressionof Classical Mechanics. From the information theory point of view, the . is a measure of uncertainty or randomness of a probability system, and was introduced and studied for the first time by Onicescu [67, 68] in the mid-1960s.作者: epinephrine 時間: 2025-3-25 12:10
978-3-319-38162-6Springer International Publishing Switzerland 2014作者: 新星 時間: 2025-3-25 18:49
Ovidiu Calin,Constantin Udri?teComprehensive treatment of probability theory from the framework of differential geometry.Well-chosen problems covering a diverse spectrum of topics.Use of hands-on software to clarify and understand 作者: ENDOW 時間: 2025-3-25 22:36
https://doi.org/10.1007/978-3-319-07779-6Entropy; Fisher information; Informational geometry; Probability density function; Statistical manifolds作者: SPASM 時間: 2025-3-26 04:00
,Epidemiologie dysthymer St?rungen,ic structure. This chapter deals with statistical models given parametrically. By specifying the parameters of a distribution, we determine a unique element of the family. When the family of distributions can be described smoothly by a set of parameters, this can be considered as a multidimensional 作者: enchant 時間: 2025-3-26 07:42
https://doi.org/10.1007/978-3-476-03356-7ack can be corrected by introducing another concept, which measures the relative entropy between two given densities. This chapter studies the Kullback–Leibler relative entropy (known also as the Kullback–Leibler divergence) between two probability densities in both discrete and continuous cases.作者: 詳細(xì)目錄 時間: 2025-3-26 12:10
https://doi.org/10.1007/978-3-663-04759-9of mean, variance, or any . moments. The solution of these variational problems belongs to the exponential family. However, explicit solutions exist only in a few particular cases. A distinguished role is played by the study of the Maxwell–Boltzmann distribution.作者: bourgeois 時間: 2025-3-26 14:21
Métabolisme et fonctions rénalestiable manifolds, tangent space, vector fields, differentiable maps, 1-forms, tensors, linear connections, Riemannian manifolds, and the Levi–Civita connection. The material of this chapter forms the basis for next chapters.作者: 向前變橢圓 時間: 2025-3-26 20:47
for the definitions is inspired from statistical models. In this new framework, the manifold of density functions is replaced by an arbitrary Riemannian manifold ., and the Fisher information matrix is replaced by the Riemannian metric . of the manifold .. The dual connections ?. and ?. are replaced作者: Devastate 時間: 2025-3-26 23:10 作者: Orthodontics 時間: 2025-3-27 01:52 作者: puzzle 時間: 2025-3-27 08:15
https://doi.org/10.33283/978-3-86298-860-0tatistical manifold or statistical model .. A contrast function, .(. | | .), for density functions ., is a smooth, non-negative function that vanishes for . = .. Eguchi [., ., .] has shown that a contrast function . induces a Riemannian metric by its second order derivatives, and a pair of dual conn作者: BRAND 時間: 2025-3-27 10:23
https://doi.org/10.1007/978-3-031-69084-6ative entropy, .-divergence, Hellinger distance, Chernoff information, Jefferey distance, Kagan divergence, and exponential contrast function. The relation with the skewness tensor and .-connection is made. The goal of this chapter is to produce hands-on examples for the theoretical concepts introdu作者: 燦爛 時間: 2025-3-27 14:27
https://doi.org/10.1007/978-3-031-69698-5 the first and second fundamental forms, curvatures, mean curvatures, and the relations among them..This material adapts the well-known theory of submanifolds to the statistical manifolds framework and consists mainly in the contributions of the authors.作者: 膠水 時間: 2025-3-27 21:36
Statistical Modelsic structure. This chapter deals with statistical models given parametrically. By specifying the parameters of a distribution, we determine a unique element of the family. When the family of distributions can be described smoothly by a set of parameters, this can be considered as a multidimensional 作者: Deject 時間: 2025-3-28 01:01 作者: alcoholism 時間: 2025-3-28 02:37
Maximum Entropy Distributionsof mean, variance, or any . moments. The solution of these variational problems belongs to the exponential family. However, explicit solutions exist only in a few particular cases. A distinguished role is played by the study of the Maxwell–Boltzmann distribution.作者: BUST 時間: 2025-3-28 07:24
An Introduction to Manifoldstiable manifolds, tangent space, vector fields, differentiable maps, 1-forms, tensors, linear connections, Riemannian manifolds, and the Levi–Civita connection. The material of this chapter forms the basis for next chapters.作者: Transfusion 時間: 2025-3-28 10:39 作者: THROB 時間: 2025-3-28 17:48 作者: 勾引 時間: 2025-3-28 19:22
Dual Laplacianshe definition and main properties of dual Laplacians and .-Laplacians. Their relationship with Hessians, curvature vector fields, and dual volume elements is emphasized..In this chapter (., ., ?, ?.) is a manifold . structured by a metric ., and endowed with a pair of dual connections ? and ?..作者: 新奇 時間: 2025-3-29 00:13
Contrast Functions Geometrytatistical manifold or statistical model .. A contrast function, .(. | | .), for density functions ., is a smooth, non-negative function that vanishes for . = .. Eguchi [., ., .] has shown that a contrast function . induces a Riemannian metric by its second order derivatives, and a pair of dual conn作者: coagulate 時間: 2025-3-29 03:11
Contrast Functions on Statistical Modelsative entropy, .-divergence, Hellinger distance, Chernoff information, Jefferey distance, Kagan divergence, and exponential contrast function. The relation with the skewness tensor and .-connection is made. The goal of this chapter is to produce hands-on examples for the theoretical concepts introdu作者: 散步 時間: 2025-3-29 09:00 作者: Epithelium 時間: 2025-3-29 11:45 作者: watertight, 時間: 2025-3-29 16:48 作者: ANTIC 時間: 2025-3-29 23:47 作者: Evocative 時間: 2025-3-30 00:31
Textbook 2014tions. This is a field that is increasingly attracting the interest of researchers from many different areas of science, including mathematics, statistics, geometry, computer science, signal processing, physics and neuroscience. It is the authors’ hope that the present book will be a valuable refere作者: Eviction 時間: 2025-3-30 07:40
f topics.Use of hands-on software to clarify and understand .This book covers topics of Informational Geometry, a field which deals with the differential geometric study of the manifold probability density functions. This is a field that is increasingly attracting the interest of researchers from ma作者: PAGAN 時間: 2025-3-30 12:07
,Epidemiologie dysthymer St?rungen,lement of the family. When the family of distributions can be described smoothly by a set of parameters, this can be considered as a multidimensional surface. We are interested in the study of the properties that do not depend on the choice of model coordinates.
