References

Lectures 丨 References

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Books, theses, overview papers

Murphy book	Kevin Patrick Murphy. “Machine Learning: a Probabilistic Perspective”. MIT Press, 2012.
EBM book	Zhijian Ou. “Energy-Based Models with Applications to Speech and Language Processing”. Foundations and Trends® in Signal Processing, 2023.
DGM review	Zhijian Ou. “A Review of Learning with Deep Generative Models from Perspective of Graphical Modeling”. 2018.
PRML book	Christopher M. Bishop. “Pattern Recognition and Machine Learning”. Springer 2006.
MacKay book	D.J. MacKay. “Information Theory, Inference, and Learning Algorithms”. Cambridge Univ. Press, 2003.
Murphy 2002-Fall-Thesis	Kevin Murphy's Ph.D. thesis 2002 其附录（Appendix）包含对图模型的表示、推理、学习的综述
GraphicalModels2004	Jordan在2004年写的综述文章：Graphical Models
FreyJojicTutorial2005	A Comparison of Algorithms for Inference and Learning in Probabilistic Graphical Models, PAMI 2005.
Salakhutdinov_phd2009_Learning Deep Generative Models	Ruslan Salakhutdinov, "Learning Deep Generative Models", Ph.D. thesis 2009.
deep_learning_英文版	DEEP LEARNING book, Bengio, Goodfellow, Courville
深度学习_中文版	DEEP LEARNING book - Bengio, Goodfellow, Courville 中文版

Representation/Semantics

CDLS_chapter4-5	CDLS book: R. G. Cowell, A. P. Dawid, S. L. Lauritzen and D. J. Spiegelhalter. "Probabilistic Networks and Expert Systems". Springer-Verlag, 1999. CDLS book Chapter 4 Graph Theory Chapter 5 Markov Properties on Graphs
Rabiner_hmm_turtorial	Rabiner's HMM tutorial 1989.
icml01_CRF	CRF: probabilistic models for segmenting and labeling sequeance data, ICML 2001.
Wallach_crf_intro_2004	H. Wallach, Conditional Random Fields: An Introduction, 2004.
tmg-thmm.rar	TMG-THMM papers B. J. Frey and N. Jojic, Transformed mixtures of Gaussians, 1999. N. Jojic, N. Petrovic, B. J. Frey and T. S. Huang, Transformed hidden Markov models: Estimating mixture models of images and inferring spatial transformations in video sequences, CVPR 2000. B. J. Frey and N. Jojic, Transformation-Invariant Clustering Using the EM Algorithm, PAMI 2003.
ieee13_Conditional random fields in speech, audio, and language processing	ieee13_Conditional random fields in speech, audio, and language processing

Exact Inference

uai96_Bucket_elimination	R. DECHTER, BUCKET ELIMINATION: A UNIFYING FRAMEWORK FOR PROBABILISTIC INFERENCE, 1996.
aij05_UnifyingCluster-TreeDecompositionsForReasoningInGMs	Kalev Kask, Rina Dechter, Javier Larrosa and Avi Dechter, Unifying Cluster-Tree Decompositions for Reasoning in Graphical models, 2005.
Factor graphs and the sum-product algorithm	Kschischang, Frey, Loeliger. Factor Graphs and the Sum-Product Algorithm, IEEE Trans. Info. Theory, 2001.
Lauritzen&Spiegelhalter88_Local Computations with Probabilities on Graphical Structures and Their Application to Expert Syst	Lauritzen and Spiegelhalter, Local Computations with Probabilities on Graphical Structures and Their Application to Expert Systems, 1988.
Shafer-Shenoy90_ProbabilityPropagation	Shafer and Shenoy, Probability Propagation, 1990.
GDL 2000	Aji and McEliece. "The generalized distributive law", IEEE Trans. Info. Theory, 2000.

Sampling/Monte Carlo Inference

MacKay_Introduction to Monte Carlo methods	MacKay, Introduction to Monte Carlo methods, 1999.
Neal93_Probabilistic Inference Using Markov Chain Monte Carlo Methods	Neal93_Probabilistic Inference Using Markov Chain Monte Carlo Methods
LiuJun2001_Monte_Carlo_Strategies_In_Scientific_Computing	LiuJun2001_Monte_Carlo_Strategies_In_Scientific_Computing

Variational Inference

MacKay_Introduction to Monte Carlo methods	Jaakkola, Tutorial on variational approximation methods, 2000.
jaakkola00_variational-tutorial	Jordan and Ghahramani, An Introduction to Variational Methods for Graphical Models, 2001.
arxiv17_Advances in Variational Inference	arxiv17_Advances in Variational Inference

Learning Basics

EM1977	A. Dempster et al., Maximum Likelihood from Incomplete Data via the EM Algorithm
Robbins&Monro1951_a stochastic approximation method	Robbins and Monro (1951). A stochastic approximation method. Ann. Math. Stat.
pietra97_inducing features of random fields	Stephen Della Pietra, Vincent Della Pietra, and John Lafferty. "Inducing features of random fields", IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997.
Gu&Kong_PNAS98_A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation prob	Gu & Kong, A stochastic approximation algorithm with Markov chain Monte-Carlo method for incomplete data estimation problems, PNAS 1998.
Delyon99_Convergence of a Stochastic Approximation Version of the EM Algorithm	Bernard Delyon, Marc Lavielle, and Eric Moulines. "Convergence of a stochastic approximation version of the EM algorithm." Annals of statistics (1999): 94-128.
JSA	Zhijian Ou, Yunfu Song. "Joint Stochastic Approximation and Its Application to Learning Discrete Latent Variable Models". UAI 2020.
DA-EBM	Xinwei Zhang, Zhiqiang Tan, Zhijian Ou. "Persistently Trained, Diffusion-assisted Energy-based Models". Stat 2023.

Bayesian Learning

ic10_nbhmm	Nan Ding, Zhijian Ou. "Variational nonparametric Bayesian hidden Markov model". ICASSP 2010.
SDM06_bayes k-means as a max-expectation algorithm	Max Welling, Kenichi Kurihara. Bayesian K-Means as a "Maximization-Expectation" Algorithm. SDM 2006.

Structure Learning

Heckerman_tr-95-06	Heckerman, A tutorial on learning with BNs, 1995
SEM.rar	Original papers on structural EM

Applications

TRF	Bin Wang, Zhijian Ou, Zhiqiang Tan. "Learning Trans-dimensional Random Fields with Applications to Language Modeling". IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2018.
nips02_lda	Blei, Ng and Jordan. Latent Dirichlet Allocation, NIPS 2002.
BleiNgJordan2003_lda	Blei, Ng and Jordan. Latent Dirichlet Allocation, Journal of Machine Learning Research, 2003.
iscslp2010_twclda	Yimin Tan, Zhijian Ou. "Topic-weak-correlated Latent Dirichlet Allocation". ISCSLP 2010.
icassp2012_crfcm	Zhijian Ou, Huaqing Luo. “CRF-based Confidence Measures of Recognized Candidates for Lattice-based Audio Indexing.” ICASSP 2012.