REFERENCES REGARDING THE FLEXIBLE BAYESIAN MODELING SOFTWARE


Neural network models.

Details of the currently-implemented network architectures are
described in the documentation here in net-models.PDF.

For a more extensive description of Bayesian neural network models and
their implementation, see my book (based on my PhD thesis):

  Neal, R. M. (1996) Bayesian Learning for Neural Networks, Lecture
  Notes in Statistics No. 118, New York: Springer-Verlag.

Neural network learning using gradient descent, early stopping, and
ensembles is discussed in the following paper:

  Neal, R. M. (1998) ``Assessing relevance determination methods using
  DELVE'', in C. M. Bishop (editor), Neural Networks and Machine
  Learning, pp. 97-129, Springer-Verlag.

I discussed neural network survival analysis models in this talk:

  Neal, R. M. (2001) ``Survival analysis using a Bayesian neural
  network'', Joint Statistical Meetings, August 2001.  Slides
  available at http://www.cs.utoronto.ca/~radford/ftp/nnsv-talk.pdf

Descriptions of the Bayesian neural network models I used to win two
prediction competitions are available here:

  Neal, R. M. and Zhang, J. (2006) ``High dimensional classification
  with Bayesian neural networks and Dirichlet diffusion trees'', in
  I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh (editors) Feature
  Extraction: Foundations and Applications, Studies in Fuzziness and
  Soft Computing, Volume 207, Springer, pp. 265-295.

  Neal, R. M. (2006) ``Classification with Bayesian neural networks'',
  in J. Quinonero-Candela, B. Magnini, I. Dagan, and F. D'Alche-Buc
  (editors) Machine Learning Challenges. Evaluating Predictive
  Uncertainty, Visual Object Classification, and Recognising Textual
  Entailment, Lecture Notes in Computer Science no. 3944,
  Springer-Verlag, pp. 28-32.


Gaussian process models.

The Gaussian process models are described in the following papers:

  Neal, R. M. (1998) ``Regression and classification using Gaussian
  process priors'' (with discussion), in J. M. Bernardo, et al
  (editors) Bayesian Statistics 6, Oxford University Press,
  pp. 475-501.

  Neal, R. M. (1997) ``Monte Carlo implementation of Gaussian process
  models for Bayesian regression and classification'', Technical
  Report No. 9702, Dept. of Statistics, University of Toronto, 24
  pages.  See http://www.cs.utoronto.ca/~radford/mc-gp.abstract.html

You might also want to read Carl Rasmussen's thesis on Evaluation of
Gaussian Processes and Other Methods for Non-Linear Regression, at
mlg.eng.cam.ac.uk/pub/pdf/Ras96b.pdf, and visit his Gaussian process
page, at http://gaussianprocess.org.


Mixture models.

The algorithms for infinite mixture models are described in the
following technical reports:

  Neal, R. M. (1998) ``Markov chain sampling methods for Dirichlet
  process mixture models'', Technical Report No. 9815, Dept. of
  Statistics, University of toronto, 17 pages.  Available at
  http://www.cs.utoronto.ca/~radford/mixmc.abstract.html


Dirichlet diffusion tree models.

Dirichlet diffusion tree models for density estimation and clustering
are described in the following papers:

  Neal, R. M. (2003) ``Density modeling and clustering using Dirichlet
  diffusion trees'', in J. M. Bernardo, et al. (editors) Bayesian
  Statistics 7, pp. 619-629.

  Neal, R. M. (2001) ``Defining priors for distributions using
  Dirichlet diffusion trees'', Technical Report No. 0104, Dept. of
  Statistics, University of Toronto, 25 pages.  Available at
  http://www.cs.utoronto.ca/~radford/dft-paper1.abstract.html

You can also get to the slides for my talk on ``Markov chain Monte
Carlo computations for Dirichlet diffusion trees'', NTOC 2001, Kyoto,
December 2001, at http://www.cs.utoronto.ca/~radford/ftp/ntoc2001.pdf


Markov chain sampling methods.

Many Markov chain methods are implemented in the software, some of
which are described in the following papers:

  Neal, R. M. (2020) ``Non-reversibly updating a uniform [0,1] value
  for Metropolis accept/reject decisions'', Technical Report, 14
  pages, http://www.cs.utoronto.ca/~radford/nrevu.abstract.html

  Neal, R. M. (2010) ``MCMC using Hamiltonian dynamics'', in the
  Handbook of Markov Chain Monte Carlo, S. Brooks, A. Gelman,
  G. L. Jones, and X.-L. Meng (editors), Chapman & Hall / CRC Press,
  pp. 113-162.  Can be obtained free as a sample chapter at
  http://www.mcmchandbook.net

  Neal, R. M. (2000) ``Slice sampling'', Technical Report No. 2005,
  Dept. of Statistics, University of Toronto, 40 pages, available
  at http://www.cs.utoronto.ca/~radford/slc-samp.abstract.html

  Neal, R. M. (2002) ``Circularly-coupled Markov chain sampling'',
  Technical Report No. 9910 (revised), Dept. of Statistics, University
  of Toronto, 49 pages, http://www.cs.utoronto.ca/~radford/circ.abstract.html

  Neal, R. M. (1998) ``Annealed importance sampling'', Technical
  Report No. 9805 (revised), Dept. of Statistics, University of
  Toronto, 25 pages, http://www.cs.utoronto.ca/~radford/ais.abstract.html

  Neal, R. M. (1994) ``Sampling from multimodal distributions using
  tempered transitions'', Technical Report No. 9421, Dept. of
  Statistics, University of Toronto, 22 pages, available at
  http://www.cs.utoronto.ca/~radford/ttrans.abstract.html

  Neal, R. M. (1994) ``An improved acceptance procedure for the hybrid
  Monte Carlo algorithm'', Journal of Computational Physics, vol. 111,
  pp. 194-203.

  Neal, R. M. (1993) Probabilistic Inference Using Markov Chain Monte
  Carlo Methods, Technical Report CRG-TR-93-1, Dept. of Computer
  Science, University of Toronto, 144 pages, available at
  http://www.cs.utoronto.ca/~radford/review.abstract.html