Radford Neal's Graduate Students

Graduate students who have finished degrees:

Alex Shestopaloff, PhD, Statistics, 2016.
Thesis: MCMC Methods for Non-linear State Space Models:

Chunyi Wang, PhD, Statistics, 2014.
Thesis: Gaussian Process Regression with Heteroscedastic Residuals and Fast MCMC Methods: PDF.

Meng Du, PhD, Statistics, 2013.
Thesis: Adjusting for Selection Bias Using Gaussian Process Models: PDF.
The R functions used in the thesis are here.

Madeleine Thompson, PhD, Statistics, 2011.
Thesis: Slice Sampling with Multivariate Steps: PDF.

Longhai Li, PhD, Statistics, 2007.
Thesis: Bayesian Classification and Regression with High-Dimensional Features: Postscript (1-sided), PDF (1-sided), Postscript (2-sided), PDF (2-sided) (also here).

Peter Liu, MSc, Computer Science, 2007 (co-supervisor with Brendan Frey).
Thesis: Using Gaussian Process Regression to Denoise Images and Remove Artefacts from Microarray Data: Postscript (30 MBytes), PDF (4 MBytes).

Babak Shahbaba, PhD, Biostatistics, 2007.
Thesis: Improving Classification Models When a Class Hierarchy is Available: pdf.

Jennifer Listgarten, PhD, Computer Science, 2006 (co-supervisor with Sam Roweis).
Thesis: Analysis of Sibling Time Series Data: Alignment and Difference Detection: pdf (60 MBytes).

Shuying Sun, PhD, Statistics, 2006 (co-supervisor with Celia Greenwood).
Thesis: Haplotype Inference Using a Hidden Markov Model with Efficient Markov Chain Sampling: pdf.

Krunoslav Kovac, MSc, Computer Science, 2005.
Thesis: Multitask Learning for Bayesian Neural Networks: Postscript, pdf.

Ruxandra Pinto, PhD, Statistics, 2002.
Thesis: Improving Markov Chain Monte Carlo Estimators Using Overrelaxation and Coupling Techniques: Postscript, pdf.
Related technical report: Improving Markov chain Monte Carlo estimators by coupling to an approximating chain.

Sonia Jain, PhD, Statistics, 2002.
Thesis: Split-Merge Techniques for Bayesian Mixture Models.
Related technical reports: A Split-Merge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model, Splitting and Merging Components of a Nonconjugate Dirichlet Process Mixture Model.

Kiam Choo, MSc, Computer Science, 2000.
Thesis: Learning Hyperparameters for Neural Network Models Using Hamiltonian Dynamics: Postscript, pdf.

Michael Harvey, MSc, Computer Science, 1999.
Thesis: Monte Carlo Inference for Belief Networks Using Coupling From the Past: Postscript, pdf.
Related conference paper: Inference for Belief Networks Using Coupling From the Past.

I also worked closely with Carl Rasmussen (PhD, CS, 1996) and Brendan Frey (PhD, ECE, 1997).

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