World's Best Scientists 2026 revealed!

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Computer Science

D-Index
42
Citations
21796
World Ranking
8135
National Ranking
488

Mathematics

D-Index
42
Citations
21818
World Ranking
1742
National Ranking
122

Overview

Christophe Andrieu is affiliated with the University of Bristol in the United Kingdom. Their primary research domain lies within the field of Mathematics, with a focus on several interconnected subfields including Statistics and Probability, Mathematical Physics, Artificial Intelligence, Condensed Matter Physics, and Economics and Econometrics.

Their body of work addresses a variety of topics, prominently featuring Markov Chains and Monte Carlo Methods, Stochastic Processes and Statistical Mechanics, Statistical Methods and Inference, Theoretical and Computational Physics, Machine Learning and Algorithms, Health Systems, Economic Evaluations, Quality of Life, and Advanced Statistical Process Monitoring.

Christophe Andrieu has contributed extensively to academic research with a notable number of publications. Some recent papers include:

  • Explicit convergence bounds for Metropolis Markov chains: Isoperimetry, spectral gaps and profiles, 2024, The Annals of Applied Probability
  • Hypocoercivity of piecewise deterministic Markov process-Monte Carlo, 2021, The Annals of Applied Probability
  • Peskun-Tierney ordering for Markovian Monte Carlo: Beyond the reversible scenario, 2021, The Annals of Statistics
  • A general perspective on the Metropolis-Hastings kernel, 2020, arXiv (Cornell University)
  • Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information, 2021, Medical Decision Making

The venues in which Christophe Andrieu frequently publishes highlight their engagement with both theoretical and applied aspects of statistics and probability. Key publication venues include:

  • arXiv (Cornell University)
  • The Annals of Applied Probability
  • Journal of the Royal Statistical Society Series B (Statistical Methodology)
  • The Annals of Statistics
  • Medical Decision Making

Throughout their research career, Christophe Andrieu has collaborated with several frequent co-authors, suggesting active participation in a network of scholars. Regular collaborators include:

  • Anthony Lee
  • Sam Power
  • Andi Q. Wang
  • Alain Durmus
  • Samuel Livingstone

Best Publications

  • On sequential Monte Carlo sampling methods for Bayesian filtering

    Arnaud Doucet;Simon Godsill;Christophe Andrieu

  • An introduction to MCMC for machine learning

    Christophe Andrieu;Nando De Freitas;Arnaud Doucet;Michael I. Jordan

  • Particle Markov chain Monte Carlo methods

    Christophe Andrieu;Arnaud Doucet;Roman Holenstein

  • A tutorial on adaptive MCMC

    Christophe Andrieu;Johannes Thoms

  • The pseudo-marginal approach for efficient Monte Carlo computations

    Christophe Andrieu;Gareth O. Roberts

  • On sequential simulation-based methods for Bayesian filtering

    Arnaud Doucet;Simon J. Godsill;Christophe Andrieu

  • Particle methods for change detection, system identification, and control

    C. Andrieu;A. Doucet;S.S. Singh;V.B. Tadic

  • On the ergodicity properties of some adaptive MCMC algorithms

    Christophe Andrieu;Éric Moulines

  • Joint Bayesian model selection and estimation of noisy sinusoids via reversible jump MCMC

    C. Andrieu;A. Doucet

  • Particle filtering for partially observed Gaussian state space models

    Christophe Andrieu;Arnaud Doucet

  • Stability of Stochastic Approximation under Verifiable Conditions

    C. Andrieu;E. Moulines;P. Priouret

  • Efficient particle filtering for jump Markov systems. Application to time-varying autoregressions

    C. Andrieu;M. Davy;A. Doucet

  • Bayesian curve fitting using MCMC with applications to signal segmentation

    E. Punskaya;C. Andrieu;A. Doucet;W.J. Fitzgerald

  • Particle methods for Bayesian modeling and enhancement of speech signals

    J. Vermaak;C. Andrieu;A. Doucet;S.J. Godsill

  • Model criticism based on likelihood-free inference, with an application to protein network evolution

    Oliver Ratmann;Christophe Andrieu;Carsten Wiuf;Sylvia Richardson

  • Particle filtering for multi-target tracking and sensor management

    A. Doucet;B.-N. Vo;C. Andrieu;M. Davy

  • Sequential MCMC for Bayesian model selection

    C. Andrieu;N. De Freitas;A. Doucet

  • Iterative algorithms for state estimation of jump Markov linear systems

    A. Doucet;C. Andrieu

  • Convergence properties of pseudo-marginal Markov chain Monte Carlo algorithms

    Christophe Andrieu;Matti Vihola

  • On-Line Parameter Estimation in General State-Space Models

    C. Andrieu;A. Doucet;V.B. Tadic

Frequent Co-Authors

Arnaud Doucet
Arnaud Doucet University of Oxford
Robert J. Piechocki
Robert J. Piechocki University of Bristol
Simon J. Godsill
Simon J. Godsill University of Cambridge
Eric Moulines
Eric Moulines Mohamed bin Zayed University of Artificial Intelligence
Nando de Freitas
Nando de Freitas DeepMind (United Kingdom)
Christian P. Robert
Christian P. Robert Paris Dauphine University
Arthur Gretton
Arthur Gretton University College London
Joe McGeehan
Joe McGeehan University of Bristol
Gareth O. Roberts
Gareth O. Roberts University of Warwick
Carsten Wiuf
Carsten Wiuf University of Copenhagen

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