- Elements of Statistical Learning 6
- Statistics 11
- Deep Learning 1
- Optimization 1
- Bayesian Nonparametrics 12
- Probabilistic Graphical Model 1
Elements of Statistical Learning
- ESL: Ch 10. Boosting and Additive Trees
- ESL: Ch 9. Additive Models, Trees, and Related Methods
- ESL: Ch 8. Model Inference and Averaging
- ESL: Ch 6. Kernel Smoothing Methods
- ESL: Ch 5. Basis Expansions and Regularization
- ESL: 시작하며
Statistics
- Bayesian shrinkage towards sharp minimaxity
- Bayesian Data Analysis 3ed(BDA3) 한국어판 발매
- Measure-Theoretic Proof of Bayes' Theorem
- Collapsed Gibbs Sampler for LDA
- Latent Dirichlet Allocation
- Reproducing Kernel Hilbert Space & Representer Theorem
- The Bootstrap Method
- Full Theoretical Explanation for EM Algorithm
- Gaussian Mixture Model
- Heuristic Derivation of Smoothing Spline
- Logistic Regression
Deep Learning
Optimization
Bayesian Nonparametrics
- Dirichlet–Laplace priors for optimal shrinkage
- Slice Sampling Methods for Dirichlet Process Mixture Model
- Sampling Methods for Dirichlet Process Mixture Models under Non-conjugate Priors
- 5. Discrete Random Structures
- 4. Dirichlet Process Mixture Model
- 3. Dirichlet Process (4)
- 3. Dirichlet Process (3)
- 3. Dirichlet Process (2)
- 3. Dirichlet Process (1)
- 2. Bayesian Finite Mixture Model
- 1. Exchangeability and De Finetti's Theorem
- Contents