信息论及数据压缩算法的介绍。 information theory,probability coding,applications of probability coding,LZ77,78, Burrows Wheeler,fractal compression.......

John M.Lee三本经典第三本，涵盖黎曼流形，联络，曲率，测地线，子流形等，有拓扑和微分流形，张量等的简介，具体可查看前两本

An Introduction to Random Matrices by G. Anderson

Introductory Econometrics for Finance 3rd.pdf

Author: Francois Louveaux, John R. Birge Publisher: Springer (2000) Binding: Hardcover, 448 pages pricer: $119.00 ISBN-10: 0387982175 editorialreviews The aim of stochastic programming is to find optimal decisions in problems which involve uncertain data. This field is currently developing rapidly with contributions from many disciplines including operations research, mathematics, and probability. Conversely, it is being applied in a wide variety of subjects ranging from agriculture to financial planning and from industrial engineering to computer networks. This textbook provides a first course in stochastic programming suitable for students with a basic knowledge of linear programming, elementary analysis, and probability. The authors aim to present a broad overview of the main themes and methods of the subject. Its prime goal is to help students develop an intuition on how to model uncertainty into mathematical problems, what uncertainty changes bring to the decision process, and what techniques help to manage uncertainty in solving the problems. The first chapters introduce some worked examples of stochastic programming and demonstrate how a stochastic model is formally built. Subsequent chapters develop the properties of stochastic programs and the basic solution techniques used to solve them. Three chapters cover approximation and sampling techniques and the final chapter presents a case study in depth. A wide range of students from operations research, industrial engineering, and related disciplines will find this a well-paced and wide-ranging introduction to this subject.

Introduction to Bayesian Analysis

Introduction to Structural Dynamics and Aeroelasticity

这是结构优化界大牛写的一本结构优化入门教材，对初学者特别有用。

这里是 ShowMeAI 持续分享的【开源eBook】系列！内容覆盖机器学习、深度学习、数据科学、数据分析、大数据、Keras、TensorFlow、PyTorch、强化学习、数学基础等各个方向。整理自各平台的原作者公开分享（审核大大请放手） ◉ 简介：这是本书的第二版，旨在为所有相关学科的读者提供一个清晰的、简单的强化学习关键思想&算法的说明。书籍在第一版的基础上，增加了近些年新的研究主题，内容重点放在核心的算法上。 ◉ 目录： 介绍 多臂老虎机 有限马尔可夫决策过程 动态规划 蒙特卡罗方法 时差学习 n-step Bootstrapping 使用表格方法进行规划和学习 带近似的策略预测 带近似的策略控制 带近似的离策略方法 资格痕迹 策略梯度方法 心理学 神经科学 应用与案例研究 前沿

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