通过状态空间方法的时间序列分析

介绍数学物理联系的一本好书，推荐给大家，希望对大家有所帮助

comment copied from amazon 2001: I brought this book ( volume 2 also as well ) because of its " fame ", but when I read it, it has several draw backs. First, may be the original version is in German, so even with good translation, it seem does not fit in the usual English style we get used to. Also the topics it choose is too few and also the area covered is too narrow and not well co-ordinated. For example, the whole volume I is almost dedicated to Calculus of variation only. In volume 2, the whole book is dedicated to differential equations. But that is not the greatest drawback. The most bad point is that the book just presents formulae after formulae, equations after equations, without giving examples of how to use it, and also no exercise for me to practice.

Nonparametric methods

Inverse Problem Theory and Methods for Model Parameter Estimation - A. Tarantola(牛叉)

Applied Numerical Methods with MATLAB for Engineers & Scientists, Chapra, MG, 2011. 很好的matlab学习教材，英文版的

The Bayesian method is the natural approach to inference, yet it is hidden from readers behind chapters of slow, mathematical analysis. The typical text on Bayesian inference involves two to three chapters on probability theory, then enters what Bayesian inference is. Unfortunately, due to mathematical intractability of most Bayesian models, the reader is only shown simple, artificial examples. This can leave the user with a so-what feeling about Bayesian inference. In fact, this was the author's own prior opinion.

使用TensorLy的Python中的Tensor方法 该存储库包含一系列有关张量学习的教程和示例，以及使用在Python中的实现以及如何使用 ， 和框架作为后端将张量方法与深度学习结合在一起。 安装 您将需要安装TensorLy的最新版本才能按照说明中的运行这些示例。 最简单的方法是克隆存储库： git clone https://github.com/tensorly/tensorly cd tensorly pip install -e . 然后只需克隆此存储库： git clone https://github.com/JeanKossaifi/tensorly_notebooks 您准备好出发了！ 目录 1-张量基础 2-张量分解 塔克分解 3-张量回归 低秩张量回归 4-Tensor方法和MXNet后端的深度学习 通过梯度下降的塔克分解 张量回归网络 5-使用PyT

An adequate presentation of any science cannot consist of detailed information alone, however extensive. It must also provide a proper view of the essential nature of the science as a whole. The purpose of the present chapter is to give a general picture of the essential nature of mathematics. For this purpose there is no great need to introduce any of the details of recent mathematical theories, since elementary mathematics and the history of the science already provide a sufficient foundation for general conclusions.

The first version of this book was a set of lecture notes for a graduate course on data mining and applications in science and technology organized by the Swedish National Graduate School in Scientific Computing (NGSSC). Since then the material has been used and further developed for an undergraduate course on numerical algorithms for data mining and IT at Link¨oping University. This is a second course in scientific computing for computer science students.