统计学自学指南
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    stats-self-learning
    stats-self-learning
    • 前言
    • 一个仅供参考的学习建议
      • MIT 18.01/18.02: Calculus
      • MIT 18.06: Linear Algebra
      • Algebra
      • MIT 18.781: Theory Of Numbers
      • MIT 18.03: Differential Equations
      • MIT RES.18-009: Learn Differential Equations: Up Close With Gilbert Strang And Cleve Moler
      • MIT 18.065: Matrix Methods In Data Analysis, Signal Processing, And Machine Learning
      • Stanford CS205A: Mathematical Methods for Computer Vision, Robotics, and Graphics
      • UW CSE559I: The art and science of positive definite matrices
      • MIT 18.330 : Introduction to numerical analysis
      • NTHU MATH2810: Probability
      • Yale Statistics251/551: Stochastic Process
      • MIT 6.262:Discrete Stochastic Processes
      • UIUC Math564: Applied Stochastic Processes
      • UCB CS126: Probability theory
      • MIT 6.041SC: Probabilistic Systems Analysis And Applied Probability
      • Supplementary
      • UA Stat571: Probability and Measure
      • IMPA: Measure Theory
      • Supplementary
      • NTHU MATH2820: Statistics
      • UCB Statistics210: Theoretical Statistics
      • UW STAT512: Introduction to Statistical Inference
      • Stanford Stat300: Theory of Statistics
      • Supplementary
      • Stanford EE364A: Convex Optimization
      • CMU Convex Optimization
      • NTHU STAT5191: Applied Multivariate Analysis
      • NTU Machine Learning
      • Coursera: Machine Learning
      • Stanford CS229: Machine Learning
      • Supplementary
      • UA Stat479: Time Series Analysis
      • UCB CS70: discrete Math and probability theory
      • MIT 6.050J: Information theory and Entropy
      • The Information Theory, Pattern Recognition, and Neural Networks
      • UCB Data100: Principles and Techniques of Data Science
      • UCB Data 8: Foundations of Data Science
      • UA Stat568: Design and Analysis of Experiments
      • NTHU STAT5510: Experimental Design and Analysis
      • NTHU STAT5410: Linear Models
      • UA Stat378: Applied Regression Analysis
      • Yale Statistics312/612: Linear Models

    Supplementary

    Probability Tutorials

    b站皇太妃太难当了的数学分析视频

    Stein实分析

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