Radford M. Neal

发表

Conclusions and Further Work

Radford M. Neal, 1996 .

Ecient Bayesian inference for stochastic volatility models with ensemble MCMC methods

Radford M. Neal, Alexander Y. Shestopaloff, 2014, 1412.3013.

Pattern Recognition and Machine Learning, by Christopher M. Bishop

Radford M. Neal, 2007 .

Sampling from multimodal distributions using tempered transitions

Radford M. Neal, 1996, Stat. Comput..

Evaluation of Neural Network Models

Radford M. Neal, 1996 .

VISUALISING A SIMULATION USING ANIMATED PICTURES

Radford M. Neal, Brian Wyvill, Graham Birtwistle, 1983 .

Nonlinear Models Using Dirichlet Process Mixtures

Babak Shahbaba, Radford M. Neal, B. Shahbaba, 2007, J. Mach. Learn. Res..

FactorAnalysisUsingDelta-RuleWake-SleepLearning

Peter Dayan, Radford M. Neal, P. Dayan, 1996 .

Difference detection in LC-MS data for protein biomarker discovery

Radford M. Neal, Jennifer Listgarten, Sam T. Roweis, 2007, Bioinform..

Factor Analysis Using Delta-Rule Wake-Sleep Learning

Peter Dayan, Radford M. Neal, P. Dayan, 1997, Neural Computation.

Corrigendum: Adaptive Rejection Metropolis Sampling

Walter R. Gilks, Radford M. Neal, Nicky Best, 1997 .

Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals pdf

Radford M. Neal, Chunyi Wang, Chunyi Wang, 2012, ArXiv.

Monte Carlo Implementation

Radford M. Neal, 1996 .

Covariance-Adaptive Slice Sampling

Radford M. Neal, Madeleine Thompson, Madeleine B. Thompson, 2010, 1003.3201.

MCMC for non-Linear State Space Models Using Ensembles of Latent Sequences

Radford M. Neal, Alexander Y. Shestopaloff, 2013 .

Connectionist Learning of Belief Networks

Radford M. Neal, 1992, Artif. Intell..

5 Using Hamiltonian Dynamics

Radford M. Neal, 2011 .

Optimal Proposal Distributions and Adaptive MCMC

Radford M. Neal, 2011 .

Bayesian Learning via Stochastic Dynamics

Radford M. Neal, 1992, NIPS.

Markov Chain Monte Carlo in Practice: A Roundtable Discussion

Bradley P. Carlin, Radford M. Neal, Robert E. Kass, 1998 .

A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants

Geoffrey E. Hinton, Radford M. Neal, 1998, Learning in Graphical Models.

High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees

Radford M. Neal, Jianguo Zhang, Jianguo Zhang, 2006, Feature Extraction.

A method for avoiding bias from feature selection with application to naive Bayes classification models

Jianguo Zhang, Radford M. Neal, Longhai Li, 2007 .

On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note)

Radford M. Neal, 2000, J. Artif. Intell. Res..

Probabilistic Inference Using Markov Chain Monte Carlo Methods

Radford M. Neal, 2011 .

Split Hamiltonian Monte Carlo

Babak Shahbaba, Radford M. Neal, Shiwei Lan, 2011, Stat. Comput..

A Non-Reversible Markov Chain Sampling Method

Radford M. Neal, Persi Diaconis, Susan Holmes, 1997 .

Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function

Radford M. Neal, 1997 .

Improving Markov chain Monte Carlo Estimators by Coupling to an Approximating Chain

Radford M. Neal, Ruxandra L. Pinto, Ruxandra L. Pinto, 2001 .

Arithmetic coding for data compression

Ian H. Witten, Radford M. Neal, John G. Cleary, 1987, CACM.

An editor for trees

Radford M. Neal, 1980 .

Bayesian Mixture Modeling

Radford M. Neal, 1992 .

Comments on 'A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images'

Radford M. Neal, 1993, IEEE Trans. Inf. Theory.

Asymmetric Parallel Boltzmann Machines are Belief Networks

Radford M. Neal, 1992, Neural Computation.

Sampling Latent States for High-Dimensional Non-Linear State Space Models with the Embedded HMM Method

Radford M. Neal, Alexander Y. Shestopaloff, 2016, Bayesian Analysis.

JADE: a simulation & software prototyping environment

Brian Wyvill, Ian H. Witten, Radford M. Neal, 1983 .

source code for arithmetic coding

Ian H. Witten, Alistair Moffat, Radford M. Neal, 1995 .

Bayesian Learning for Neural Networks

Radford M. Neal, 1995 .

Bayesian Methods for Adaptive Models

Peter Cheeseman, Radford M. Neal, Haim Sompolinsky, 2011 .

Representing numeric data in 32 bits while preserving 64-bit precision pdf

Radford M. Neal, 2015, ArXiv.

Transferring Prior Information Between Models Using Imaginary Data

Radford M. Neal, 2001 .

Fast exact summation using small and large superaccumulators pdf

Radford M. Neal, 2015, ArXiv.

Annealed importance sampling

Radford M. Neal, 1998, Stat. Comput..

Gene function classification using Bayesian models with hierarchy-based priors

Babak Shahbaba, Radford M. Neal, B. Shahbaba, 2006, BMC Bioinformatics.

Classification with Bayesian Neural Networks

Radford M. Neal, 2005, MLCW.

Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models

Radford M. Neal, Matthew J. Beal, Sam T. Roweis, 2003, NIPS.

The Helmholtz Machine

Geoffrey E. Hinton, Peter Dayan, Richard S. Zemel, 1995, Neural Computation.

Compressing Parameters in Bayesian High-order Models with Application to Logistic Sequence Models ∗

Radford M. Neal, Longhai Li, Longhai Li, 2008 .

Modeling Dyadic Data with Binary Latent Factors

Zoubin Ghahramani, Radford M. Neal, Sam T. Roweis, 2006, NIPS.

Multiple Alignment of Continuous Time Series

Radford M. Neal, Jennifer Listgarten, Sam T. Roweis, 2004, NIPS.

Bayesian training of backpropagation networks by the hybrid Monte-Carlo method

Radford M. Neal, 1992 .

Jade: a distributed software prototyping environment

Brian Wyvill, Ian H. Witten, Radford M. Neal, 1983, OPSR.

Bayesian Learning for Neural Networks

Geoffrey E. Hinton, Radford M. Neal, 1995 .

Arithmetic coding revisited

Ian H. Witten, Alistair Moffat, Radford M. Neal, 1995, Proceedings DCC '95 Data Compression Conference.

Assessing Relevance determination methods using DELVE

Radford M. Neal, 1998 .

Regression and Classification Using Gaussian Process Priors

Radford M. Neal, 2009 .

Non-reversibly updating a uniform [0, 1] value for Metropolis accept/reject decisions pdf

Radford M. Neal, 2020, ArXiv.

Pattern Recognition and Machine Learning

Radford M. Neal, 2007, Technometrics.

Inference for Belief Networks Using Coupling From the Past pdf

Radford M. Neal, Michael Harvey, Michael Harvey, 2000, UAI.

Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure

Radford M. Neal, Jennifer Listgarten, Sam T. Roweis, 2006, NIPS.

The delve manual

Geoffrey E. Hinton, Robert Tibshirani, Zoubin Ghahramani, 1996 .

ARITHMETIC CODING REVISITED (EXTENDED ABSTRACT)

Alistair Moffat, Radford M. Neal, Ian H. Wittent, 1995 .

Arithmetic coding revisited

Ian H. Witten, Alistair Moffat, Radford M. Neal, 1998, TOIS.

Slice Sampling

Radford M. Neal, 2003, The Annals of Statistics.

Markov Chain Sampling Methods for Dirichlet Process Mixture Models

Radford M. Neal, 2000 .

Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification

Radford M. Neal, 1997, physics/9701026.

Near Shannon limit performance of low density parity check codes

Radford M. Neal, D. Mackay, 1996 .

Bayesian Mixture Modeling by Monte Carlo Simulation

Radford M. Neal, 1991 .

Priors for Infinite Networks

Radford M. Neal, 1996 .

Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior

Radford M. Neal, B. Shahbaba, 2005, math/0510449.

Haplotype inference using a Bayesian Hidden Markov model

Radford M. Neal, Shuying Sun, C. Greenwood, 2007, Genetic epidemiology.

A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model

Radford M. Neal, Sonia Jain, 2004 .

Bayesian Learning for Neural Networks (Lecture Notes in Statistical Vol. 118)

Radford M. Neal, B. Warner, 1997 .

The Computational Complexity of Taxonomic Inference a Taxonomic Inference System

Radford M. Neal, 1989 .

Taking Bigger Metropolis Steps by Dragging Fast Variables

Radford M. Neal, 2005, math/0502099.

Splitting and merging components of a nonconjugate Dirichlet process mixture model

Radford M. Neal, Sonia Jain, 2007 .

On Bayesian inference for the M/G/1 queue with efficient MCMC sampling

Radford M. Neal, Alexander Y. Shestopaloff, 2014, 1401.5548.

The Short-Cut Metropolis Method

Radford M. Neal, 2005, math/0508060.

Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better

Radford M. Neal, 2004, math/0407281.

Slice Sampling with Adaptive Multivariate Steps: The Shrinking-Rank Method

Radford M. Neal, Madeleine B. Thompson, 2010, 1011.4722.

Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation

Radford M. Neal, R. M. Neal, 1995, Learning in Graphical Models.

A data-calibrated distribution of deglacial chronologies for the North American ice complex from glaciological modeling

Radford M. Neal, W. R. Peltier, Lev Tarasov, 2012 .

A Method for Compressing Parameters in Bayesian Models with Application to Logistic Sequence Prediction Models

Radford M. Neal, Longhai Li, 2007, 0711.4983.

Markov Chain Sampling for Non-linear State Space Models Using Embedded Hidden Markov Models

Radford M. Neal, 2003, math/0305039.

MCMC Using Hamiltonian Dynamics

Radford M. Neal, 2011, 1206.1901.

A new view of the EM algorithm that justifies incremental and other variants

Radford M. Neal, 1993 .

MCMC Using Ensembles of States for Problems with Fast and Slow Variables such as Gaussian Process Regression

Radford M. Neal, 2011, 1101.0387.

ANALYSIS OF A NONREVERSIBLE MARKOV CHAIN SAMPLER

Radford M. Neal, P. Diaconis, S. Holmes, 2000 .

Estimating Ratios of Normalizing Constants Using Linked Importance Sampling

Radford M. Neal, 2005, math/0511216.

MCMC methods for Gaussian process models using fast approximations for the likelihood

Radford M. Neal, Chunyi Wang, Chunyi Wang, 2013, 1305.2235.

An improved acceptance procedure for the hybrid Monte Carlo algorithm

Radford M. Neal, 1992, hep-lat/9208011.

An improved acceptance procedure for the hybrid Monte Carlo algorithm

Radford M. Neal, 1992, hep-lat/9208011.

Puzzles of Anthropic Reasoning Resolved Using Full Non-indexical Conditioning

Radford M. Neal, 2006, math/0608592.

Circularly-Coupled Markov Chain Sampling

Radford M. Neal, 2017, 1711.04399.

The Short-Cut Metropolis Method

Radford M. Neal, 2005, math/0508060.

Bayesian learning for neural networks

Radford M. Neal, 1995 .

Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification

Radford M. Neal, 1997, physics/9701026.

Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better

Radford M. Neal, 2004, math/0407281.

Puzzles of Anthropic Reasoning Resolved Using Full Non-indexical Conditioning

Radford M. Neal, 2006, math/0608592.

Slice Sampling

Radford M. Neal, 2000, physics/0009028.

Estimating Ratios of Normalizing Constants Using Linked Importance Sampling

Radford M. Neal, 2005, math/0511216.

Taking Bigger Metropolis Steps by Dragging Fast Variables

Radford M. Neal, 2005, math/0502099.

Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation

Radford M. Neal, 1995, Learning in Graphical Models.

A method for avoiding bias from feature selection with application to naive Bayes classification models

Radford M. Neal, Longhai Li, Jianguo Zhang, 2007, math/0702591.

5 MCMC Using Hamiltonian Dynamics

Radford M. Neal, 2011 .

How to View an MCMC Simulation as a Permutation, with Applications to Parallel Simulation and Improved Importance Sampling

Radford M. Neal, 2012, 1205.0070.

Proceedings of the IXth International Conference on High Energy Accelerators, Stanford, CA, May 2-7, 1974

Radford M. Neal, R. Neal, 2006 .

Bayesian learning for neural networks

Radford M. Neal, 1995 .

MCMC for non-Linear State Space Models Using Ensembles of Latent Sequences

Radford M. Neal, Alexander Y. Shestopaloff, 2013, 1305.0320.

Markov Chain Sampling for Non-linear State Space Models Using Embedded Hidden Markov Models

Radford M. Neal, 2003, math/0305039.

Pattern Recognition and Machine Learning

Radford M. Neal, 2007, Technometrics.

UniversityAnalysis of a Non-Reversible Markov Chain SamplerPersi

Radford M. Neal, P. Diaconis, D. Aldous, 1997 .

Pattern recognition and machine learning

Radford M. Neal, Vijay K. Vemuri, James L. Crowley, 2019, Springer US.

Pattern Recognition and Machine Learning

Nasser M. Nasrabadi, Radford M. Neal, Christopher M. Bishop, 2006, Technometrics.

MCMC using Hamiltonian dynamics

Radford M. Neal, 2012, 1206.1901.

Slice Sampling

Radford M. Neal, 2003, The Annals of Statistics.

MCMC Using Hamiltonian Dynamics

Radford M. Neal, M. Huber, 2011 .

Key Generation for Text Encryption in Cellular Networks using Multi-point Crossover Function

C.-C. Jay Kuo, Radford M. Neal, Suresh Chandra Satapathy, 2011 .

Near Shannon Limit Performance of Low Density Parity Check Codes

Radford M. Neal, C. J., MacKayCavendish, 1996 .

Markov Chain Monte Carlo in Practice: A Roundtable Discussion

Radford M. Neal, A. Gelman, R. Kass, 1998 .

Density Modeling and Clustering Using Dirichlet Diffusion Trees

Radford M. Neal, M. J. Bayarri, D. Heckerman, 2003 .

Pattern Recognition and Machine Learning

Radford M. Neal, José Eduardo Ochoa Luna, Maria Bernadete Zanusso, 2006, Technometrics.