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This page contains resources about Bayesian Nonparametrics.

SubfieldsEdit

See Category:Bayesian Nonparametrics for some of its subfields.

  • Chinese Restaurant Process (CRP)
  • Chinese Restaurant Franchise (CRF)
  • Indian Buffet Process (IBP)
  • Pitman–Yor Process
  • Hierarchical Dirichlet Process (HDP)
  • Mixture of Dirichlet Processes (MDP)
  • Dirichlet Processes Mixture Model (DPMM)
  • CRP Mixture Model
  • IBP Latent Factor Model
  • Latent Dirichlet Allocation (LDA)
  • Lévy Process
  • Bernoulli Process
  • Completely Random Measures
    • Poisson Random Measure / Poisson Point Process
    • Gamma Process
    • Beta Process / Beta-Bernoulli Process
    • Stable Process 
  • Pólya Trees
  • Pólya's Urn Process
  • Hoppe's Urn Process
  • Stick Breaking Process

Online CoursesEdit

Video LecturesEdit


Lecture NotesEdit

Books and Book Chapters Edit

  • Küchler, U., & Sorensen, M. (1997). Exponential families of stochastic processes. Springer Science & Business Media.
  • Dey, D. D., MüIler, P., & Sinha, D. (Eds.). (1998). Practical nonparametric and semiparametric Bayesian statistics (Vol. 133). Springer Science & Business Media.
  • Ghosh, J. K., & Ramamoorthi, R. V. (2003). Bayesian Nonparametrics.Springer Series in Statistics. Springer-Verlag, New York16, 37.
  • Görür, D. (2007). Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning. PhD Dissertation. TU Berlin.
  • Koller, D., & Friedman, N. (2009). "Section 19.5: Learning Models with Hidden Variables ". Probabilistic Graphical Models. MIT Press.
  • Hjort, N. L., Holmes, C., Müller, P., & Walker, S. G. (Eds.). (2010). Bayesian Nonparametrics. Cambridge University Press.
  • Orbanz, P., & Teh, Y. W. (2011). Bayesian Nonparametric Models. In Encyclopedia of Machine Learning (pp. 81-89). Springer US.
  • Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. Chapter 25: Clustering. MIT Press.
  • Jordan, M. I. (2013). Hierarchical models, nested models and completely random measures. Frontiers of Statistical Decision Making and Bayesian Analysis: in Honor of James O. Berger. New York: Springer, 207-218.
  • Theodoridis, S. (2015). "Section 13.12: Nonparametric Bayesian Modeling". Machine Learning: A Bayesian and Optimization Perspective. Academic Press.
  • Müller, P., Quintana, F. A., Jara, A., & Hanson, T. (2015). Nonparametric Bayesian Data Analysis. New York: Springer.
  • Phadia, E. G. (2015). Prior Processes and Their Applications: Nonparametric Bayesian Estimation. Springer.
  • Mitra, R., & Müller, P. (Eds.). (2015). Nonparametric Bayesian Inference in Biostatistics. Springer.
  • Goodman, N. D., & Tenenbaum, J. B. (2016). "Chapter 12: Non-parametric models".  Probabilistic Models of Cognition. 2nd Ed. (link)

Scholarly Articles Edit

See also NPBayes 2008 for more references.

  • Damien, P. (2005). Some Bayesian Nonparametric Models. Handbook of statistics25, 279-314.
  • Hanson, T. E., Branscum, A. J., & Johnson, W. O. (2005). Bayesian nonparametric modeling and data analysis: an introduction. Handbook of statistics25, 245-278.
  • Walker, S. (2005). Bayesian Nonparametric Inference. Handbook of statistics25, 339-371.
  • Sudderth, E.B. (2006). Graphical Models for Visual Object Recognition and Tracking. Ph.D. dissertation, Massachusetts Institute of Technology.
  • Görür, D. (2007). Nonparametric Bayesian Discrete Latent Variable Models for Unsupervised Learning. Ph.D. dissertation, Max Planck Institute for Biological Cybernetics.
  • Thibaux, R. J. (2008). Nonparametric Bayesian Models for Machine Learning. Ph.D. dissertation, Department of Statistics, University of California, Berkeley.
  • Frigyik, B. A., Kapila, A., & Gupta, M. R. (2010). Introduction to the dirichlet distribution and related processes. Department of Electrical Engineering, University of Washington. UWEETR-2010-0006.
  • Gershman, S. J., & Blei, D. M. (2012). A tutorial on Bayesian Nonparametric Models. Journal of Mathematical Psychology56(1), 1-12.
  • Ghahramani, Z. (2013). Bayesian non-parametrics and the probabilistic approach to modelling. Phil. Trans. R. Soc. A371, 20110553.

TutorialsEdit

SoftwareEdit

See alsoEdit

Other Resources Edit