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