Why Is Radford M. Neal Influential?
According to Wikipedia , Radford M. Neal is a professor 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. He studied computer science at the University of Calgary and at the University of Toronto . He has made great contributions in the area of machine learning and statistics, where he is particularly well known for his work on Markov chain Monte Carlo, error correcting codes and Bayesian learning for neural networks. He is also known for his blog and as the developer of pqR: a new version of the R interpreter.
Radford M. Neal's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
1980 1990 2000 2010 2020 0 2500 5000 7500 10000 12500 15000 17500 20000 22500 Published Papers Pattern Recognition and Machine Learning (2007) Arithmetic coding for data compression (1987) Near Shannon limit performance of low density parity check codes (1996) A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants (1998) MCMC Using Hamiltonian Dynamics (2011) Markov Chain Sampling Methods for Dirichlet Process Mixture Models (2000) Annealed importance sampling (2001) The Helmholtz Machine (1995) Probabilistic Inference Using Markov Chain Monte Carlo Methods (2011) (1112)Markov Chain Monte Carlo in Practice: A Roundtable Discussion (1998) Connectionist Learning of Belief Networks (1992) Arithmetic coding revisited (1998) A Split-Merge Markov chain Monte Carlo Procedure for the Dirichlet Process Mixture Model (2004) Monte Carlo Implementation of Gaussian Process Models for Bayesian Regression and Classification (1997) (458)Sampling from multimodal distributions using tempered transitions (1996) A data-calibrated distribution of deglacial chronologies for the North American ice complex from glaciological modeling (2012) Nonlinear Models Using Dirichlet Process Mixtures (2009) ANALYSIS OF A NONREVERSIBLE MARKOV CHAIN SAMPLER (2000) A new view of the EM algorithm that justifies incremental and other variants (1993) (211)Bayesian Learning via Stochastic Dynamics (1992) (202)Priors for Infinite Networks (1996) Bayesian Methods for Adaptive Models (2011) (180)Bayesian training of backpropagation networks by the hybrid Monte-Carlo method (1992) (160)Multiple Alignment of Continuous Time Series (2004) (156)Bayesian Mixture Modeling (1992) Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation (1998) An improved acceptance procedure for the hybrid Monte Carlo algorithm (1992) Splitting and merging components of a nonconjugate Dirichlet process mixture model (2007) Modeling Dyadic Data with Binary Latent Factors (2006) Regression and Classification Using Gaussian Process Priors (2009) (113)Optimal Proposal Distributions and Adaptive MCMC (2011) (111)Assessing Relevance determination methods using DELVE (1998) (109)Markov Chain Monte Carlo Methods Based on `Slicing' the Density Function (1997) (94)Difference detection in LC-MS data for protein biomarker discovery (2007) The delve manual (1996) (76)Improving Asymptotic Variance of MCMC Estimators: Non-reversible Chains are Better (2004) (71)Split Hamiltonian Monte Carlo (2014) Estimating Ratios of Normalizing Constants Using Linked Importance Sampling (2005) (61)Bayesian Mixture Modeling by Monte Carlo Simulation (1991) (52)Factor Analysis Using Delta-Rule Wake-Sleep Learning (1997) Inferring State Sequences for Non-linear Systems with Embedded Hidden Markov Models (2003) (50)Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals (2012) (44)Monte Carlo Implementation (1996) Improving Classification When a Class Hierarchy is Available Using a Hierarchy-Based Prior (2005) High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees (2006) On Deducing Conditional Independence from d-Separation in Causal Graphs with Feedback (Research Note) (2000) Corrigendum: Adaptive Rejection Metropolis Sampling (1997) MCMC Using Ensembles of States for Problems with Fast and Slow Variables such as Gaussian Process Regression (2011) (34)Puzzles of Anthropic Reasoning Resolved Using Full Non-indexical Conditioning (2006) (34)Bayesian Learning for Neural Networks (Lecture Notes in Statistical Vol. 118) (1997) Gene function classification using Bayesian models with hierarchy-based priors (2006) 5 MCMC Using Hamiltonian Dynamics (2011) (30)Arithmetic coding revisited (1995) Taking Bigger Metropolis Steps by Dragging Fast Variables (2005) (26)Haplotype inference using a Bayesian Hidden Markov model (2007) The Short-Cut Metropolis Method (2005) (21)Bayesian Detection of Infrequent Differences in Sets of Time Series with Shared Structure (2006) On Bayesian inference for the M/G/1 queue with efficient MCMC sampling (2014) (18)Improving Markov chain Monte Carlo Estimators by Coupling to an Approximating Chain (2001) (17)Markov Chain Sampling for Non-linear State Space Models Using Embedded Hidden Markov Models (2003) (16)Transferring Prior Information Between Models Using Imaginary Data (2001) (16)Fast exact summation using small and large superaccumulators (2015) (15)MCMC for non-Linear State Space Models Using Ensembles of Latent Sequences (2013) 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) A method for avoiding bias from feature selection with application to naive Bayes classification models (2007) Asymmetric Parallel Boltzmann Machines are Belief Networks (1992) Sampling Latent States for High-Dimensional Non-Linear State Space Models with the Embedded HMM Method (2018) Slice Sampling with Adaptive Multivariate Steps: The Shrinking-Rank Method (2010) (11)Covariance-Adaptive Slice Sampling (2010) (9)Jade: a distributed software prototyping environment (1983) How to View an MCMC Simulation as a Permutation, with Applications to Parallel Simulation and Improved Importance Sampling (2012) (8)MCMC methods for Gaussian process models using fast approximations for the likelihood (2013) (8)Compressing Parameters in Bayesian High-order Models with Application to Logistic Sequence Models ∗ (2008) Classification with Bayesian Neural Networks (2005) Ecient Bayesian inference for stochastic volatility models with ensemble MCMC methods (2014) (5)Inference for Belief Networks Using Coupling From the Past (2000) (4)Non-reversibly updating a uniform [0, 1] value for Metropolis accept/reject decisions (2020) (4)Comments on 'A theoretical analysis of Monte Carlo algorithms for the simulation of Gibbs random field images' (1993) The Computational Complexity of Taxonomic Inference a Taxonomic Inference System (1989) (3)VISUALISING A SIMULATION USING ANIMATED PICTURES (1983) An editor for trees (1980) JADE: a simulation & software prototyping environment (1983) Proceedings, 9th International Conference on the High-Energy Accelerators (HEACC 1974) : Stanford, California, May 2-7, 1974 (1974) (2)Proceedings of the IXth International Conference on High Energy Accelerators, Stanford, CA, May 2-7, 1974 (2006) A Method for Compressing Parameters in Bayesian Models with Application to Logistic Sequence Prediction Models (2007) Evaluation of Neural Network Models (1996) ARITHMETIC CODING REVISITED (EXTENDED ABSTRACT) (1995) (1)A Non-Reversible Markov Chain Sampling Method (1997) (1)Software for flexible Bayesian learning (1997) (0)Conclusions and Further Work (1996) Proceedings, 9th International Conference on the High-Energy Accelerators (HEACC 1974) (1974) (0)FactorAnalysisUsingDelta-RuleWake-SleepLearning (1996) (0)5 Using Hamiltonian Dynamics (2011) (0)2 2 0 A ug 1 99 2 An Improved Acceptance Procedure for the Hybrid Monte Carlo Algorithm (1992) (0)Answers to a 2008 Applied Statistics Comprehensive Exam Question From the University of Toronto PhD Comprehensive Exam in Applied Statistics , 2008 (2008) (0)Some Notes for the BIRS Workshop on Statistical Inference for High Energy Physics (0)Computing Lik elihoodFunctions for High-Energy PhysicsExperiments whenDistrib utions areDefinedby Simulators with NuisanceParameters (2008) (0)Representing numeric data in 32 bits while preserving 64-bit precision (2015) (0)Approximating the Likelihood for the Hyper-parameters in Gaussian Process Regression (2010) (0)BILEVEL DISPLAY OF CONTINUOUS- TONE IMAGES USING PEANO CURVES (1981) Tutorial on Exact Sampling Methods , (0)source code for arithmetic coding (1995) (0)Pattern Recognition and Machine Learning, by Christopher M. Bishop (2007) (0)Non-reversibly updating a uniform [0, 1] value for accept/reject decisions (2019) (0)More Papers This paper list is powered by the following services:
Other Resources About Radford M. Neal What Schools Are Affiliated With Radford M. Neal? Radford M. Neal is affiliated with the following schools:
Radford M. Neal's AcademicInfluence.com Rankings 
