| Sampling latent states for high-dimensional non-linear state space models with the embedded HMM method AY Shestopaloff, RM Neal Bayesian Analysis 13 (3), 797-822, 2018 | 6 | 2018 |
| Circularly-coupled Markov chain sampling RM Neal arXiv preprint arXiv:1711.04399, 2017 | 13 | 2017 |
| Fast exact summation using small and large superaccumulators RM Neal arXiv preprint arXiv:1505.05571, 2015 | 16 | 2015 |
| Representing numeric data in 32 bits while preserving 64-bit precision RM Neal arXiv preprint arXiv:1504.02914, 2015 | | 2015 |
| Efficient Bayesian inference for stochastic volatility models with ensemble MCMC methods AY Shestopaloff, RM Neal arXiv preprint arXiv:1412.3013, 2014 | 6 | 2014 |
| Split hamiltonian monte carlo B Shahbaba, S Lan, WO Johnson, RM Neal Statistics and Computing 24 (3), 339-349, 2014 | 55 | 2014 |
| On Bayesian inference for the M/G/1 queue with efficient MCMC sampling AY Shestopaloff, RM Neal arXiv preprint arXiv:1401.5548, 2014 | 11 | 2014 |
| MCMC methods for Gaussian process models using fast approximations for the likelihood C Wang, RM Neal arXiv preprint arXiv:1305.2235, 2013 | 9 | 2013 |
| MCMC for non-linear state space models using ensembles of latent sequences AY Shestopaloff, RM Neal arXiv preprint arXiv:1305.0320, 2013 | 13 | 2013 |
| Gaussian process regression with heteroscedastic or non-gaussian residuals C Wang, RM Neal arXiv preprint arXiv:1212.6246, 2012 | 28 | 2012 |
| How to view an MCMC simulation as a permutation, with applications to parallel simulation and improved importance sampling RM Neal arXiv preprint arXiv:1205.0070, 2012 | 9 | 2012 |
| A data-calibrated distribution of deglacial chronologies for the North American ice complex from glaciological modeling L Tarasov, AS Dyke, RM Neal, WR Peltier Earth and Planetary Science Letters 315, 30-40, 2012 | 182 | 2012 |
| MCMC Using Hamiltonian Dynamics R Neal Handbook of Markov Chain Monte Carlo, 113-162, 2011 | 1587 | 2011 |
| MCMC using ensembles of states for problems with fast and slow variables such as Gaussian process regression RM Neal arXiv preprint arXiv:1101.0387, 2011 | 33 | 2011 |
| Slice sampling with adaptive multivariate steps: The shrinking-rank method MB Thompson, RM Neal arXiv preprint arXiv:1011.4722, 2010 | 12 | 2010 |
| Covariance-adaptive slice sampling M Thompson, RM Neal arXiv preprint arXiv:1003.3201, 2010 | 10 | 2010 |
| Nonlinear models using Dirichlet process mixtures B Shahbaba, R Neal Journal of Machine Learning Research 10 (Aug), 1829-1850, 2009 | 176 | 2009 |
| Compressing parameters in Bayesian high-order models with application to logistic sequence models L Li, RM Neal Bayesian Analysis 3 (4), 793-821, 2008 | 8 | 2008 |
| A method for avoiding bias from feature selection with application to naive bayes classification models L Li, J Zhang, RM Neal Bayesian Analysis 3 (1), 171-196, 2008 | 11 | 2008 |
| Computing likelihood functions for high-energy physics experiments when distributions are defined by simulators with nuisance parameters R Neal CERN, 2008 | 6 | 2008 |
Ian H. WittenProfessor of Computer Science, University of Waikato, New ZealandVerified email at cs.waikato.ac.nz
