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Jeffrey S. Rosenthal

Jeffrey S. Rosenthal

D-Index & Metrics

Mathematics

D-Index
48
Citations
14970
World Ranking
1181
National Ranking
41

Research.com Recognitions

  • 2012 - Fellow of the Royal Society of Canada Academy of Science
  • 2007 - COPSS Presidents' Award For fundamental contributions to probability theory, stochastic processes, and Markov Chain Monte Carlo (MCMC) algorithms, with applications to statistics; for seminal contributions to the theoretical underpinnings of the convergence rates of MCMC algorithms; for his prolific record of collaboration, resulting in significant publications in economics, mathematical finance, artificial intelligence, and survival analysis; for outstanding mentoring; and for his extraordinary skill at the communication of some of the deeper ideas of our discipline through the media (print, radio, and television) and through the publication of a general audience book on probability in real life which, less than 2 years after publication, is in its 6th printing.

Overview

Jeffrey S. Rosenthal is affiliated with the University of Toronto in Canada and has a significant research output in the fields of Mathematics and Computer Science. Their work prominently features in subfields such as Statistics and Probability, Artificial Intelligence, Mathematical Physics, Computer Vision and Pattern Recognition, and Economics and Econometrics.

The main topics addressed by their research include:

  • Markov Chains and Monte Carlo Methods
  • Statistical Methods and Inference
  • Bayesian Methods and Mixture Models
  • Stochastic processes and statistical mechanics
  • Machine Learning and Algorithms
  • Theoretical and Computational Physics
  • Sports Analytics and Performance

They are frequently published in venues such as:

  • arXiv (Cornell University)
  • Journal of Applied Probability
  • Communications in Statistics - Simulation and Computation
  • Canadian Journal of Statistics
  • Methodology And Computing In Applied Probability

Recent publications include:

  • Optimal scaling of random-walk metropolis algorithms on general target distributions, 2020, Stochastic Processes and their Applications
  • Dimension-Free Mixing for High-Dimensional Bayesian Variable Selection, 2022, Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • Ergodicity of Markov Processes via Nonstandard Analysis, 2021, Memoirs of the American Mathematical Society
  • Capturing spatial dependence of COVID-19 case counts with cellphone mobility data, 2021, Spatial Statistics
  • Jump Markov chains and rejection-free Metropolis algorithms, 2021, Computational Statistics

Their frequent co-authors include:

  • Gareth O. Roberts
  • Aki Dote
  • Hirotaka Tamura
  • Ali Sheikholeslami
  • Jun Yang

Awards received by Jeffrey S. Rosenthal include:

  • Fellow of the Royal Society of Canada (2012), Academy of Science
  • COPSS Presidents' Award (2007), for fundamental contributions to probability theory, stochastic processes, and Markov Chain Monte Carlo (MCMC) algorithms, with applications to statistics; for seminal contributions to the theoretical underpinnings of the convergence rates of MCMC algorithms; for prolific collaboration resulting in publications in economics, mathematical finance, artificial intelligence, and survival analysis; for mentoring; and for communication of key discipline ideas through media and a general audience book on probability

Best Publications

  • Optimal scaling for various Metropolis-Hastings algorithms

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • Examples of Adaptive MCMC

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • General state space Markov chains and MCMC algorithms

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • Optimal scaling of discrete approximations to Langevin diffusions

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo

    Jeffrey S. Rosenthal

  • COUPLING AND ERGODICITY OF ADAPTIVE MARKOV CHAIN MONTE CARLO ALGORITHMS

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • On adaptive Markov chain Monte Carlo algorithms

    Yves F. Atchadé;Jeffrey S. Rosenthal

  • Link analysis ranking: algorithms, theory, and experiments

    Allan Borodin;Gareth O. Roberts;Jeffrey S. Rosenthal;Panayiotis Tsaparas

  • Geometric Ergodicity and Hybrid Markov Chains

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • Finding authorities and hubs from link structures on the World Wide Web

    Allan Borodin;Gareth O. Roberts;Jeffrey S. Rosenthal;Panayiotis Tsaparas

  • Finding Generators for Markov Chains via Empirical Transition Matrices, with Applications to Credit Ratings

    Robert B. Israel;Jeffrey S. Rosenthal;Jason Z. Wei

  • Convergence rates for Markov chains

    Jeffrey S. Rosenthal

  • A First Look at Rigorous Probability Theory

    Jeffrey Seth Rosenthal

  • Markov‐chain monte carlo: Some practical implications of theoretical results

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • On the efficiency of pseudo-marginal random walk Metropolis algorithms

    Chris Sherlock;Alexandre H. Thiery;Gareth O. Roberts;Jeffrey S. Rosenthal

  • Probability and Statistics: The Science of Uncertainty

    Michael J. Evans;Jeffrey S. Rosenthal

  • Convergence of slice sampler Markov chains

    Gareth O. Roberts;Jeffrey S. Rosenthal

  • Learn From Thy Neighbor: Parallel-Chain and Regional Adaptive MCMC

    Radu V. Craiu;Jeffrey Rosenthal;Chao Yang

  • Active Learning Strategies in Advanced Mathematics Classes.

    Jeffrey S. Rosenthal

  • Harris recurrence of Metropolis-within-Gibbs and trans-dimensional Markov chains

    Gareth O. Roberts;Jeffrey S. Rosenthal

Frequent Co-Authors

Gareth O. Roberts
Gareth O. Roberts University of Warwick
James Allen Fill
James Allen Fill Johns Hopkins University
Xiao-Li Meng
Xiao-Li Meng Harvard University
Johan Segers
Johan Segers Université Catholique de Louvain
Robin Pemantle
Robin Pemantle University of Pennsylvania
Panayiotis Tsaparas
Panayiotis Tsaparas University of Ioannina
Eric Moulines
Eric Moulines Mohamed bin Zayed University of Artificial Intelligence
Allan Borodin
Allan Borodin University of Toronto
Arnaud Doucet
Arnaud Doucet University of Oxford
Bani K. Mallick
Bani K. Mallick Texas A&M University

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