# Radford M. Neal

#11,491

Most Influential Person Now

Canadian computer scientist

## Radford M. Neal's AcademicInfluence.com Rankings

Radford M. Nealcomputer-science Degrees

Computer Science

#598

World Rank

#618

Historical Rank

Database

#515

World Rank

#539

Historical Rank

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Computer Science

## Why Is Radford M. Neal Influential?

(Suggest an Edit or Addition)According to Wikipedia, Radford M. Neal is a professor emeritus at the Department of Statistics and Department of Computer Science at the University of Toronto, where he holds a research chair in statistics and machine learning.

## Radford M. Neal's Published Works

### Published Works

- Pattern Recognition and Machine Learning (2007) (23511)
- Bayesian Learning for Neural Networks (1995) (3435)
- Arithmetic coding for data compression (1987) (3294)
- A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants (1998) (2700)
- MCMC Using Hamiltonian Dynamics (2011) (2656)
- Near Shannon limit performance of low density parity check codes (1996) (2444)
- Markov Chain Sampling Methods for Dirichlet Process Mixture Models (2000) (2011)
- Bayesian learning for neural networks (1995) (1577)
- Annealed importance sampling (1998) (1323)
- The Helmholtz Machine (1995) (1247)
- Probabilistic Inference Using Markov Chain Monte Carlo Methods (2011) (1230)
- Slice Sampling (2003) (1110)
- Markov Chain Monte Carlo in Practice: A Roundtable Discussion (1998) (646)
- Connectionist Learning of Belief Networks (1992) (623)
- Arithmetic coding revisited (1995) (607)
- A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model (2004) (510)
- Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification (1997) (484)
- Sampling from multimodal distributions using tempered transitions (1996) (379)
- A data-calibrated distribution of deglacial chronologies for the North American ice complex from glaciological modeling (2012) (320)
- Bayesian Methods for Adaptive Models (2011) (293)
- Nonlinear Models Using Dirichlet Process Mixtures (2007) (271)
- Priors for Infinite Networks (1996) (270)
- ANALYSIS OF A NONREVERSIBLE MARKOV CHAIN SAMPLER (2000) (267)
- Bayesian Learning via Stochastic Dynamics (1992) (237)
- A new view of the EM algorithm that justifies incremental and other variants (1993) (232)
- Modeling Dyadic Data with Binary Latent Factors (2006) (176)
- Optimal Proposal Distributions and Adaptive MCMC (2011) (175)
- Bayesian training of backpropagation networks by the hybrid Monte-Carlo method (1992) (175)
- An improved acceptance procedure for the hybrid Monte Carlo algorithm (1992) (173)
- Bayesian Mixture Modeling (1992) (172)
- Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation (1995) (163)
- Multiple Alignment of Continuous Time Series (2004) (159)
- Regression and Classification Using Gaussian Process Priors (2009) (157)
- Splitting and merging components of a nonconjugate Dirichlet process mixture model (2007) (150)
- Assessing Relevance determination methods using DELVE (1998) (112)
- Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function (1997) (104)
- Difference detection in LC-MS data for protein biomarker discovery (2007) (89)
- Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better (2004) (89)
- Split Hamiltonian Monte Carlo (2011) (85)
- The delve manual (1996) (79)
- Estimating Ratios of Normalizing Constants Using Linked Importance Sampling (2005) (64)
- Monte Carlo Implementation (1996) (56)
- Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models (2003) (52)
- Factor Analysis Using Delta-Rule Wake-Sleep Learning (1997) (52)
- Bayesian Mixture Modeling by Monte Carlo Simulation (1991) (52)
- Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals (2012) (49)
- On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note) (2000) (47)
- High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees (2006) (40)
- MCMC Using Ensembles of States for Problems with Fast and Slow Variables such as Gaussian Process Regression (2011) (40)
- Taking Bigger Metropolis Steps by Dragging Fast Variables (2005) (40)
- Bayesian Learning for Neural Networks (Lecture Notes in Statistical Vol. 118) (1997) (40)
- Puzzles of Anthropic Reasoning Resolved Using Full Non-indexical Conditioning (2006) (40)
- Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior (2005) (39)
- Corrigendum: Adaptive Rejection Metropolis Sampling (1997) (36)
- Gene function classification using Bayesian models with hierarchy-based priors (2006) (30)
- Haplotype inference using a Bayesian Hidden Markov model (2007) (25)
- On Bayesian inference for the M/G/1 queue with efficient MCMC sampling (2014) (23)
- Markov Chain Sampling for Non-linear State Space Models Using Embedded Hidden Markov Models (2003) (21)
- The Short-Cut Metropolis Method (2005) (21)
- MCMC for non-Linear State Space Models Using Ensembles of Latent Sequences (2013) (20)
- Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure (2006) (20)
- Improving Markov chain Monte Carlo Estimators by Coupling to an Approximating Chain (2001) (18)
- Transferring Prior Information Between Models Using Imaginary Data (2001) (18)
- Fast exact summation using small and large superaccumulators (2015) (17)
- Sampling Latent States for High-Dimensional Non-Linear State Space Models with the Embedded HMM Method (2016) (14)
- A method for avoiding bias from feature selection with application to naive Bayes classification models (2007) (13)
- 5 MCMC Using Hamiltonian Dynamics (2011) (13)
- Circularly-Coupled Markov Chain Sampling (2017) (13)
- Computing Likelihood Functions for High-Energy Physics Experiments when Distributions are Defined by Simulators with Nuisance Parameters (2008) (13)
- Slice Sampling with Adaptive Multivariate Steps: The Shrinking-Rank Method (2010) (12)
- Asymmetric Parallel Boltzmann Machines are Belief Networks (1992) (12)
- How to View an MCMC Simulation as a Permutation, with Applications to Parallel Simulation and Improved Importance Sampling (2012) (11)
- MCMC methods for Gaussian process models using fast approximations for the likelihood (2013) (10)
- Covariance-Adaptive Slice Sampling (2010) (9)
- Jade: a distributed software prototyping environment (1983) (8)
- Compressing Parameters in Bayesian High-order Models with Application to Logistic Sequence Models ∗ (2008) (7)
- Classification with Bayesian Neural Networks (2005) (7)
- Non-reversibly updating a uniform [0, 1] value for Metropolis accept/reject decisions (2020) (6)
- Ecient Bayesian inference for stochastic volatility models with ensemble MCMC methods (2014) (5)
- The Computational Complexity of Taxonomic Inference a Taxonomic Inference System (1989) (4)
- Inference for Belief Networks Using Coupling From the Past (2000) (4)
- JADE: a simulation & software prototyping environment (1983) (3)
- Comments on 'A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images' (1993) (3)
- VISUALISING A SIMULATION USING ANIMATED PICTURES (1983) (3)
- An editor for trees (1980) (2)
- Proceedings, 9th International Conference on the High-Energy Accelerators (HEACC 1974) : Stanford, California, May 2-7, 1974 (1974) (2)
- A Method for Compressing Parameters in Bayesian Models with Application to Logistic Sequence Prediction Models (2007) (1)
- Evaluation of Neural Network Models (1996) (1)
- Pattern Recognition and Machine Learning, by Christopher M. Bishop (2007) (1)
- A Non-Reversible Markov Chain Sampling Method (1997) (1)
- ARITHMETIC CODING REVISITED (EXTENDED ABSTRACT) (1995) (1)
- Proceedings of the IXth International Conference on High Energy Accelerators, Stanford, CA, May 2-7, 1974 (2006) (1)
- Non-reversibly updating a uniform [0, 1] value for accept/reject decisions (2019) (0)
- Computing Lik elihoodFunctions for High-Energy PhysicsExperiments whenDistrib utions areDefinedby Simulators with NuisanceParameters (2008) (0)
- FactorAnalysisUsingDelta-RuleWake-SleepLearning (1996) (0)
- Some Notes for the BIRS Workshop on Statistical Inference for High Energy Physics (0)
- Software for flexible Bayesian learning (1997) (0)
- 5 Using Hamiltonian Dynamics (2011) (0)
- Proceedings, 9th International Conference on the High-Energy Accelerators (HEACC 1974) (1974) (0)
- Tutorial on Exact Sampling Methods , (0)
- Conclusions and Further Work (1996) (0)
- Representing numeric data in 32 bits while preserving 64-bit precision (2015) (0)
- Answers to a 2008 Applied Statistics Comprehensive Exam Question From the University of Toronto PhD Comprehensive Exam in Applied Statistics , 2008 (2008) (0)
- Split Hamiltonian Monte Carlo (2013) (0)
- Approximating the Likelihood for the Hyper-parameters in Gaussian Process Regression (2010) (0)
- BILEVEL DISPLAY OF CONTINUOUS- TONE IMAGES USING PEANO CURVES (1981) (0)

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