Notes

My Research Notes

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1. The Log-Sum-Exp Trick 📕
2. Hidden Markov Model and Driving Behavior Modeling: From HMMs to Factorial HMMs to FHMM–IDM — a three–part primer 📙📘📗
3. Bayesian inference and conjugate priors Planned
4. Prior settings matter in Bayesian inference (variance) Planned
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. Fundamental Probabilistic Graphical Models: Tail-to-Tail, Head-to-Tail, and Head-to-Head 📙📕
13. Introduction to Autoregressive (AR) Processes 📕
14. Bayesian calibration of car-following models Planned
15. Connections among AR processes, Cochrane-Orcutt correction, Ornstein-Uhlenbeck processes, and Gaussian Processes 📙📕📘
16. Matrix derivative of Frobenius norm involving Hadamard product 📕
17. 《社会型交互与自动驾驶：综述》（知乎） 📘📗
18. 多输出高斯过程 (multiple output GP)（知乎） 📙

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Collected Online Blogs and Books (by other researchers)

1. Bayesian Data Analysis
2. Bayesian Neural Networks
3. Pattern Recognition and Machine Learning (PRML)
4. Spatiotemporal Data Modeling
5. Probabilistic Artificial Intelligence
6. 如何努力成为一个 Top Ph.D. Student
7. Sharpen your scientific plotting with an artist’s eye — plottie.art
8. Optimization Bootcamp
9. Tensor Decompositions for Data Science
10. Color palettes — Paul Tol’s notes