应用线性回归模型 非常好的一本书，国外很多大学选作为回归分析课的教材，值得好好研读。

WINNER II interim channel models信道建立模型PDF

20170512-110547（MS-Celeb-1M数据集训练的模型文件，微软人脸识别数据库，名人榜选择前100万名人，搜索引擎采集每个名人100张人脸图片。预训练模型准确率0.993+-0.004）

Dirichlet Process Mixture Models(DPMM)的Matlab程序

微软MCSA认证考试70-768 Developing SQL Data Models Course OutlineModule 1: Introduction to Business Intelligence and Data ModelingThis module introduces key BI concepts and the Microsoft BI product suite.Lessons Introduction to Business Intelligence The Microsoft business intelligence platform Lab : Exploring a BI Solution Exploring a Data Warehouse Exploring a data model After completing this module, students will be able to: Describe BI scenarios, trends, and project roles. Describe the products that make up the Microsoft BI platform. Module 2: Creating Multidimensional Databases This module describes how to create multidimensional databases using SQL Server Analysis Services.Lessons Introduction to Multidimensional Analysis Data Sources and Data Source Views Cubes Overview of Cube Security Configure SSAS Monitoring SSAS Lab : Creating a multidimensional database Creating a Data Source Creating and Configuring a data Source View Creating and Configuring a Cube Adding a Dimension to a Cube After completing this module, you will be able to: Describe considerations for a multidimensional database. Create data sources and data source views. Create a cube Implement security in a multidimensional database. Configure SSAS to meet requirements including memory limits, NUMA and disk layout. Monitor SSAS performance. Module 3: Working with Cubes and DimensionsThis module describes how to implement dimensions in a cube.Lessons Configuring Dimensions Defining Attribute Hierarchies Implementing Sorting and Grouping Attributes Slowly Changing Dimensions Lab : Working with Cubes and Dimensions Configuring Dimensions Defining Relationships and Hierarchies Sorting and Grouping Dimension Attributes After completing this module, you will be able to: Configure dimensions. Define attribute hierarchies. Implement sorting and grouping for attri

Hastie的成名作，扩展的加性模型，从统计角度看待Adaboost。成书于1990年。 Hastie. Generalized Additive Models, Chapman and Hall, 1990.

GMM算法经典论文，opencv中的GMM算法应该就是参考这边文章。自己做的中文翻译pdf也附在了里面

Neuronal Dynamics From Single Neurons to Networks and Models of Cognition.pdf Neuronal Dynamics From Single Neurons to Networks and Models of Cognition.pdf

Matrix-geometric solutions in stochastic models an algorithmic approach

Introduction to Probability Models (Sheldon M. Ross) 第11版 英文版