| Slides |
要点 |
Readings |
|
lesson01.pdf
|
图模型入门，综述 |
KF book 1.2 |
|
lesson02_Semantics.pdf
|
图模型语义
- 有向（无环）概率图模型
- 无向概率图模型
|
Required:
- CDLS book Chapter 5
- Murphy book 10.1 DGMs
- Murphy book 19.2 UGMs
Optional:
- Bishop book 8.1 - 8.3
|
|
lesson03_HMM_CRF.pdf
|
举例：隐马尔科夫模型（Hidden Markov Models, HMM），条件随机场（Conditional Random Fields, CRF） |
Required:
- Rabiner's HMM tutorial 1989.
- CRF: probabilistic models for segmenting and labeling sequeance data, ICML
- H. Wallach, Conditional Random Fields: An Introduction, 2004.
- TMG-THMM
Optional:
- Murphy book 17.3 Hidden Markov models (HMMs)
- Murphy book 19.6 Conditional random fields (CRFs)
|
|
lesson04_mlEstimate.pdf
|
最大似然参数估计 |
Required:
- Murphy book 11.4 The EM algorithm
Optional:
- A. Dempster et al., EM algorithm original paper
- Gu & Kong, SA 1998.
- Delyon, Lavielle and Moulines, SAEM, 1999.
|
|
lesson05_RFLearning.pdf
|
无向概率图模型参数估计 |
Required:
- Murphy book 19.3 Parameterization of MRFs
- Murphy book 19.5 Learning
Optional:
- Robbins and Monro (1951) SA
- Inducing features of random fields 1997
- Ruslan Salakhutdinov Ph.D. Thesis 2.1 Restricted Boltzmann Machines
- Trans-dimensional Random Field Language Model ACL2015
- TCRF-based Confidence Measures ICASSP2012
|
|
lesson06_ve.pdf
|
变量消除算法 — 图模型（精确）推理算法的早期形式，易于理解 |
Required:
- DECHTER, BUCKET ELIMINATION, 1996
- Rabiner's HMM tutorial 1989.
Optional:
- Murphy book 20.3 The variable elimination algorithm
|
|
lesson07_ClusterTree.pdf
|
图模型（精确）推理算法的完整介绍 |
Required:
- Unifying Cluter-Tree Decomposition 2005.
- Factor Graphs and the Sum-Product Algorithm, 2001
Optional:
- Probability Propagation, 1990.
- Murphy book 20.4 The junction tree algorithm
|
|
lesson08_triangulate.pdf
|
三角化 |
Required:
- Murphy thesis 2002 B.3 From graph to junction tree
Optional:
- CDLS book 4.4 From chain graph to junction tree
|
|
lesson09_Kalman.pdf
|
含有连续变量的图模型的推理 |
Required:
- Murphy book 18.3 Inference in LG-SSM
- Murphy thesis 2002 B.5 Message passing with continuous random variables
Optional:
- Murphy book 18.5 Approximate online inference for non-linear, non-Gaussian SSMs
|
|
lesson10_sampling.pdf
|
近似推理算法 — 采样近似 |
Required:
- Murphy book 23 Monte Carlo Inference
- Murphy book 24 Markov Chain Monte Carlo (MCMC) Inference
Optional:
- MacKay, Introduction to Monte Carlo methods, 1999.
|
|
lesson11_variational.pdf
|
近似推理算法 — 变分近似 |
Required:
- Murphy book 21 Variational Inference
- Murphy book 22 More Variational Inference
Optional:
- Jaakkola, Tutorial on variational approximation methods, 2000.
- Jordan and Ghahramani, An Introduction to Variational Methods for GMs, 2001
|
|
lesson12_BayesEstimate.pdf
|
贝叶斯学习 |
Required:
- Murphy book 5 Bayesian Statistics
- MacKay book 28 Model Comparison and Occam's Razor
Optional:
- Bishop book 2.Probability Distributions
- Variational nonparametric Bayesian hidden Markov Model 2010.
- Bayesian K-Means 2006.
|
|
lesson13_StructureLearning.pdf
|
结构学习 |
Required:
- Murphy book 26 Graphical Model Structure Learning
Optional:
- Heckerman, A tutorial on learning with BNs, 1995
- Original papers on structural EM
|
|
lesson14_lda.pdf
|
TopicModeling - 一个综合的例子，展示在实际中运用图模型的表示、推理和学习理论 |
Required:
- Blei, Ng and Jordan. Latent Dirichlet Allocation, NIPS 2002.
Optional:
- Blei, Ng and Jordan. Latent Dirichlet Allocation, JMLR 2003.
- Topic-weak-correlated LDA 2010.
|