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Differential-geometrical methods in statistics by Amari S.

Differential-geometrical methods in statistics Amari S. ebook
Page: 301
Format: djvu
ISBN: 0387860662,
Publisher: Springer

Partha Niyogi's very lucid talk entitled Geometric Methods and Manifold Learning includes a brief and very basic introduction to differential geometry(starts at t=40:49) which I found helpful. Differential geometry in 10 slides. For the rest of this post we will assume the setting of In all but the simplest cases an analytic solution will not exist and we will need to resort to numerical methods. For instance, finding maximum likelihood and maximum Along the way we will introduce some concepts from differential geometry and derive more efficient gradient directions to follow. Print this page Share · Home / Mathematics & Statistics / Finite Mathematics The level of mathematical expertise required is limited to differential and matrix calculus. He used differential geometry method. This research thrust is concerned with stochastic partial differential equations, measure-valued stochastic processes, weakly interacting stochastic systems, and topics in applied probability. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. The requirements are invariance under 1-1 transformations and invariance under sufficient statistics. Fomenko Differential Geometry and Topology Plenum Publishing Corporation. The various stages necessary for the implementation of the method are clearly identified, with a chapter given over to each one: approximation, construction of the integral forms, matrix organization, solution of the algebraic systems and architecture of programs. Differential-Geometrical Methods in Statistics (Lecture Notes in Statistics 28) by Shun-ichi Amari in Back Matter; See all 8 books this book cites. A common activity in statistics and machine learning is optimization.