Radford M. Neal

Q: What Schools Are Affiliated With Radford M. Neal

Radford M. Neal is affiliated with the following schools: University of Calgary, University of Toronto

Radford M. Neal's AcademicInfluence.com Rankings

Radford M. Neal

Computer Science

#598

World Rank

#618

Historical Rank

Database

#515

World Rank

#539

Historical Rank

computer-science Degrees

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Computer Science

Why Is Radford M. Neal Influential?

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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.

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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

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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What Schools Are Affiliated With Radford M. Neal?

Radford M. Neal is affiliated with the following schools: