Notes

My Research Notes (click here for all posts):

📙: Bayesian Learning; 📕: Math Tricks; 📘: Driving Behaviors; 📗: Research;

1. The Log-Sum-Exp Trick;📕
2. Forward and backward algorithm in Hidden Markov Models (HMM): To be updated;
3. Bayesian inference and conjugate priors: To be updated;
4. Prior settings matter in Bayesian inference (variance): To be updated;
5. Heterogeneity and Hierarchical Models;📙
6. Random Effects and Hierarchical Models in Driving Behaviors Modeling;📙📘
7. Proof: unbiasedness of ordinary least squares (OLS);📕
8. From ordinary least squares (OLS) to generalized least squares (GLS);📕
9. Modeling Autocorrelation: FFT vs Gaussian Processes;📙📕
10. Gaussian Processes (GP) for Time Series Forecasting;📙
11. A Detailed Introduction to Gaussian Velocity Fields (GVF) Based on Gaussian Processes;📙📘📗
12. Autoregressive (AR) processes: To be updated;
13. Bayesian calibration of car-following models: To be updated;
14. Connections among AR processes, Cochrane-Orcutt correction, Ornstein-Uhlenbeck processes, and Gaussian Processes; 📙📕📘
15. Matrix Derivative of Frobenius norm involving Hadamard Product;📕
16. 《社会型交互与自动驾驶：综述》 - Enzo的文章 - 知乎;📘📗
17. 多输出高斯过程 (multiple output GP) - Enzo的文章 - 知乎;📙

Recommended Online Blogs and Books (by others):

1. Bayesian Network;
2. Pattern Recognition and Machine Learning (PRML) ;
3. Spatiotemporal Data Modeling;