高斯过程 回归 分类的经典书籍还有相关的工具包 Gaussian Processes for Machine Learning Carl Edward Rasmussen Christopher K. I. Williams The MIT Press

高斯过程回归与分类 的经典程序 与The MIT Press出版的书籍的程序代码 (书籍可参考我上传的pdf文档)

This definitive textbook provides a solid introduction to discrete and continuous stochastic processes, tackling a complex field in a way that instils a deep understanding of the relevant mathematical principles, and develops an intuitive grasp of the way these principles can be applied to modelling real-world systems. It includes a careful review of elementary probability and detailed coverage of Poisson, Gaussian and Markov processes with richly varied queuing applications. The theory and applications of inference, hypothesis testing, estimation, random walks, large deviations, martingales and investments are developed. Written by one of the world's leading information theorists, evolving over twenty years of graduate classroom teaching and enriched by over 300 exercises, this is an exceptional resource for anyone looking to develop their understanding of stochastic processes.

Fourth Edition，Henry Stark Illinois Institute of Technology John W. Woods Rensselaer Polytechnic Institute

kkphoon文章给出了基于K-L展开的非高斯非平稳随机过程模拟（kkphoon的该篇文章在matlab中的实现可在我另一份上传资源里找到，对于协方差函数的特征函数与特征值的数值解，另一份资源中也将给出）

The availability of large data sets has allowed researchers to uncover complex properties such as large-scale fluctuations and heterogeneities in many networks, leading to the breakdown of standard theoretical frameworks and models. Until recently these systems were considered as haphazard sets of points and connections. Recent advances have generated a vigorous research effort in understanding the effect of complex connectivity patterns on dynamical phenomena. This book presents a comprehensive account of these effects. A vast number of systems, from the brain to ecosystems, power grids and the Internet, can be represented as large complex networks. This book will interest graduate students and researchers in many disciplines, from physics and statistical mechanics, to mathematical biology and information science. Its modular approach allows readers to readily access the sections of most interest to them, and complicated maths is avoided so the text can be easily followed by non-experts in the subject.

Probability And Random Processes For Electrical And Computer Engineers 的习题答案 该书作者是：John A. Gubner, 剑桥大学出版社

PDF版， Henry Stark John W. Woods第四版 Probability, Statistics, and Random Processes for Engineers 习题解答。

美国Polytechnic University的Athanasios Papoulis教授的经典教材配套习题答案

INSTRUCTOR'S SOLUTIONS MANUAL Probability, Statistics, and Random Processes for Electrical Engineering_A. Leon-Garcia，国外的随机课程经典教材，这个文档是课后习题答案