Titlebook: Geometric Modeling in Probability and Statistics; Ovidiu Calin,Constantin Udri?te Textbook 2014 Springer International Publishing Switzerl

具體

nominal

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.

epinephrine

978-3-319-38162-6Springer International Publishing Switzerland 2014

新星

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

ENDOW

https://doi.org/10.1007/978-3-319-07779-6Entropy; Fisher information; Informational geometry; Probability density function; Statistical manifolds

SPASM

,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

enchant

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.

詳細(xì)目錄

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.

bourgeois

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.

向前變橢圓

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