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@STRING{aij = {Artificial Intelligence}}

@STRING{annrevpsy = {Annual Review of Psychology}}

@STRING{bbs = {Behavioral and Brain Sciences}}

@STRING{bc = {Biological Cybernetics}}

@STRING{cogpsych = {Cognitive Psychology}}

@STRING{cvpr = {Proc. IEEE Conference on Computer Vision and Pattern Recognition}}

@STRING{devpsych = {Developmental Psychology}}

@STRING{ijcai = {Proceedings of the International Joint Conference on Artificial Intelligence}}

@STRING{jepgen = {Journal of Experimental Psychology: General}}

@STRING{jmathpsych = {Journal of Mathematical Psychology}}

@STRING{ml = {Machine Learning}}

@STRING{nc = {Neural Computation}}

@STRING{nips = {Advances in Neural Information Processing Systems}}

@STRING{pami = {IEEE Pat. Anal. Mach. Intell.}}

@STRING{proccogsci = {Proceedings of the Annual Meeting of the Cognitive Science Society}}

@STRING{procieee = {Proceedings of the IEEE}}

@STRING{psybullrev = {Psychonomic Bulletin and Review}}

@STRING{psyrev = {Psychological Review}}

@STRING{psysci = {Psychological Science}}

@STRING{tics = {Trends in Cognitive Science}}

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}

@TECHREPORT{2004,
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  title = {A direct formulation for sparse {PCA} using semidefinite programming},
  institution = {Computer Science Division, University of California, Berkeley},
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  number = {UCB/CSD-04-1330}
}

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  title = {Combined models for topic spotting and topic-dependent language modeling.},
  booktitle = {1997 {IEEE} {W}orkshop on {A}utomatic {S}peech {R}ecognition and
	{U}nderstanding {P}roceedings},
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  pages = {535-542}
}

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}

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@INCOLLECTION{Blei2004,
  author = {D. Blei and T. Griffiths and M. Jordan and J. Tenenbaum},
  title = {Hierarchical topic models and the nested {C}hinese restaurant process},
  booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 16},
  publisher = {MIT Press},
  year = {2004},
  address = {Cambridge, MA}
}

@ARTICLE{Blei2005,
  author = {D. Blei and M. Jordan},
  title = {Variational inference for {D}irichlet process mixtures},
  journal = {Journal of {B}ayesian Analysis},
  year = {2005},
  volume = {1},
  pages = {121-144},
  number = {1}
}

@INCOLLECTION{Blei2004a,
  author = {D. M. Blei and T. L. Griffiths and M. I. Jordan and J. B. Tenenbaum},
  title = {Hierarchical topic models and the nested {C}hinese restaurant process},
  booktitle = {Advances in {N}eural {I}nformation {P}rocessing {S}ystems 16},
  publisher = {MIT Press},
  year = {2004},
  address = {Cambridge, MA}
}

@ARTICLE{Blei2006,
  author = {Blei, D. M. and Jordan, M. I.},
  title = {Variational inference for {D}irichlet process mixtures},
  journal = {Bayesian Analysis},
  year = {2006},
  volume = {1},
  pages = {121--144},
  number = {1},
  file = {blei2006vid.pdf:blei2006vid.pdf:PDF},
  timestamp = {2008.01.29}
}

@ARTICLE{Blei2003,
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  year = {2003},
  volume = {3},
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}

@ARTICLE{Blei2003a,
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  year = {2003},
  volume = {3},
  pages = {993-1022}
}

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  title = {Applying discrete {PCA} in data analysis},
  booktitle = {Proceedings of the 20th {C}onference on {U}ncertainty in {A}rtificial
	{I}ntelligence {(UAI)}},
  year = {2004},
  address = {San Francisco, CA},
  publisher = {Morgan Kaufmann}
}

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  author = {Caron, F. and Davy, M. and Doucet, A.},
  title = {Generalized Polya Urn for Time-varying {D}irichlet Process Mixtures},
  booktitle = {23rd Conference on Uncertainty in Artificial Intelligence (UAI'2007),
	Vancouver, Canada, July 2007},
  year = {2007},
  abstract = {Dirichlet Process Mixtures (DPMs) are a popular class of statistical
	models to perform density estimation and clustering. However, when
	the data available have a distribution evolving over time, such models
	are inadequate. We introduce here a class of time-varying DPMs which
	ensures that at each time step the random distribution follows a
	DPM model. Our model relies on an intuitive and simple generalized
	Polya urn scheme. Inference is performed using Markov chain Monte
	Carlo and Sequential Monte Carlo. We demonstrate our model on various
	applications.},
  file = {caron2007gpu.pdf:caron2007gpu.pdf:PDF},
  owner = {sm},
  timestamp = {2008.01.22}
}

@MISC{Caron2007a,
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  title = {Stationary {P}itman-{Y}or Processes},
  howpublished = {Poster, {I}saac {N}ewton {I}nstitute Workshop on Construction and
	Properties of {B}ayesian nonparametric regression models},
  month = {August},
  year = {2007},
  owner = {davidpfau},
  timestamp = {2010.01.31}
}

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  year = {1994},
  pages = {139--152},
  publisher = {Springer}
}

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  pages = {273-302}
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@ARTICLE{Chater1996,
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@INCOLLECTION{Chater1997,
  author = {Nick Chater and Ulrike Hahn},
  title = {Representational distortion, similarity and the Universal Law of
	Generalization},
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	and Categorisation},
  publisher = {Department of Artificial Intelligence, Edinburgh University},
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	Pain},
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  address = {Edinburgh}
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  title = {Ten years of the rational analysis of cognition},
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  year = {1999},
  volume = {3},
  pages = {57-65}
}

@ARTICLE{Chater2003,
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  title = {Bayesian classification ({A}uto{C}lass): Theory and results},
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  year = {1995},
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@ARTICLE{Chen1999,
  author = {Chen, S. F. and Goodman, J.},
  title = {{An empirical study of smoothing techniques for language modeling}},
  journal = {Computer Speech and Language},
  year = {1999},
  volume = {13},
  pages = {359--394},
  number = {4},
  __markedentry = {[fwood]},
  owner = {fwood},
  timestamp = {2010.03.11}
}

@TECHREPORT{Chen1998a,
  author = {Stanley F. Chen and Joshua Goodman},
  title = {An Empirical Study of Smoothing Techniques for Language Modelling},
  institution = {Computer Science Group, Harvard University},
  year = {1998},
  __markedentry = {[fwood]},
  owner = {ucabjga},
  timestamp = {2009.05.28}
}

@TECHREPORT{Chen1998,
  author = {S. F. Chen and J. T. Goodman},
  title = {An empirical study of smoothing techniques for language modeling},
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@INCOLLECTION{Griffiths2004a,
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@BOOKLET{Griffithsinprep,
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@TECHREPORT{Griffiths2005a,
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  title = {Infinite latent feature models and the {I}ndian buffet process},
  institution = {Gatsby Computational Neuroscience Unit},
  year = {2005},
  number = {2005-001}
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@INCOLLECTION{Griffiths2005b,
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	{C}ognitive {S}cience {S}ociety},
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@BOOKLET{Griffiths2005c,
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@MISC{Griffiths2005d,
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@INCOLLECTION{Griffiths2003,
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@INPROCEEDINGS{Griffiths2002,
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  year = {2002},
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@INPROCEEDINGS{Griffiths2005e,
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