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Welcome. This is an outline of a community-driven collection of reading lists about key concepts relevant to bnpy.
Recent publications using bnpy from the Sudderth lab
Theses at Brown University
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"Variational Inference for Hierarchical Dirichlet Process Based Nonparametric Models". Undergraduate honors thesis by William Stephenson. 2015. [paper]
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"Parallelization of Variational Inference for Bayesian nonparametric topic models". Undergraduate honors theses by Sonia Phene. 2015. [paper]
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"Variational Inference for Beta-Bernoulli Dirichlet Process Mixture Models." Master's thesis by Mengrui Ni. 2015. [paper]
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"Reliable and scalable variational inference for Bayesian nonparametric models". PhD thesis proposal by Michael C. Hughes. 2014. [paper]
Conference publications
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"Memoized online variational inference for Dirichlet process mixture models." Michael C. Hughes and Erik B. Sudderth. NIPS 2013. [paper] [supplement] [bibtex]
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"Reliable and scalable variational inference for the hierarchical Dirichlet process." Michael C. Hughes, Dae Il Kim, and Erik B. Sudderth. AISTATS 2015. [paper] [supplement] [bibtex]
Related conference publications on stochastic (not memoized) variational inference
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"Efficient Online Inference for Bayesian Nonparametric Relational Models." Dae Il Kim, Prem Gopalan, David M. Blei and Erik B. Sudderth. NIPS 2013. [paper]
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"Truly Nonparametric Online Variational Inference for Hierarchical Dirichlet Processes." Michael J. Bryant and Erik B. Sudderth. NIPS 2012. [paper]
Reading Lists
Basics: Simple Clustering
Key concepts: K-means, mixture models, EM algorithm, Gaussian distribution
Basics: Topic Models
Key concepts: Dirichlet distribution, topic models, Latent Dirichlet Allocation
Advanced: Topic Models
Key concepts: online learning, correlations, metadata, nonparametric
Basics : Sequential Markov Models
Key concepts: Markov chains, Hidden Markov Models
Basics : Optimization-based Bayesian learning algorithms
Key concepts: Expectation-maximization, variational inference
Prerequisite: Basics: Simple Clustering
Basics : Dirichlet distributions
Key concepts: Gamma function, Dirichlet random variables
Advanced : Dirichlet process
Key concepts: stick breaking, Chinese restaurant process
Advanced : Nonparametric Bayesian models
TODO: [Basics : Sampling-based Bayesian learning algorithms]
TODO: [Basics : Online learning]
General references
Free textbooks
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"Bayesian Reasoning and Machine Learning" by David Barber. 2012. [pdf].
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"Information theory, inference, and learning algorithms" by David MacKay. 2003. [pdf]
Digital resources
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BNP annotated bibliography by Colorado Reed Sadly this reference is a broken link... hopefully will be resurrected soon.
Updated