G. Roberts

发表

On the Geometric Convergence of the Gibbs Sampler

Nicholas G. Polson, G. Roberts, 1994 .

Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models

G. Roberts, O. Papaspiliopoulos, 2007, 0710.4228.

Bayesian Inference For Nondecomposable Graphical Gaussian Models.

G. Roberts, Paolo Giudici, P. Dellaportas, 2003 .

Optimal scaling for various Metropolis-Hastings algorithms

J. Rosenthal, G. Roberts, 2001 .

Obituary: Richard Lewis Tweedie

Sean P. Meyn, G. Roberts, W. Dunsmuir, 2002, Journal of Applied Probability.

Random‐weight particle filtering of continuous time processes

P. Fearnhead, G. Roberts, O. Papaspiliopoulos, 2010 .

Optimal scaling of discrete approximations to Langevin diffusions

J. Rosenthal, G. Roberts, 1998 .

Exponential convergence of Langevin distributions and their discrete approximations

R. Tweedie, G. Roberts, 1996 .

Langevin Diffusions and Metropolis-Hastings Algorithms

G. Roberts, O. Stramer, 2002 .

Possible biases induced by MCMC convergence diagnostics

J. Rosenthal, G. Roberts, M. Cowles, 1999 .

A General Framework for the Parametrization of Hierarchical Models

Gareth O. Roberts, Omiros Papaspiliopoulos, Martin Skold, 2007, 0708.3797.

On inference for partially observed nonlinear diffusion models using the Metropolis–Hastings algorithm

G. Roberts, O. Stramer, 2001 .

Bayesian inference for non‐Gaussian Ornstein–Uhlenbeck stochastic volatility processes

G. Roberts, O. Papaspiliopoulos, P. Dellaportas, 2004 .

Bayesian inference for partially observed stochastic epidemics

G. Roberts, P. O’Neill, 1999 .

Non-centred parameterisations for hierarchical models and data augmentation.

Gareth O. Roberts, Omiros Papaspiliopoulos, Martin Sköld, 2003 .

Markov Chain Monte Carlo

J. Rosenthal, G. Roberts, M. Wickerhauser, 2018 .

Perfect slice samplers

G. Roberts, A. Mira, J. Møller, 2001 .

Convergence of Slice Sampler Markov Chains

J. Rosenthal, G. Roberts, 1999 .

An Approach to Diagnosing Total Variation Convergence of MCMC Algorithms

G. Roberts, P. Dellaportas, S. Brooks, 1997 .

The polar slice sampler

J. Rosenthal, G. Roberts, 2002 .

Kinetic energy choice in Hamiltonian/hybrid Monte Carlo

G. Roberts, Samuel Livingstone, Michael F Faulkner, 2017, Biometrika.

On convergence rates of Gibbs samplers for uniform distributions

J. Rosenthal, G. Roberts, 1998 .

Shift-coupling and convergence rates of ergodic averages

J. Rosenthal, G. Roberts, 1997 .

Exponential Convergence of Langevin Diiusions and Their Discrete Approximations

R. Tweedie, G. Roberts, 1997 .

The pseudo-marginal approach for efficient Monte Carlo computations

C. Andrieu, G. Roberts, 2009, 0903.5480.

Examples of Adaptive MCMC

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2009 .

Possible biases induced by MCMC convergence.

G. Roberts, K. Cowles, S. Rosenthal, 1999 .

Bounds on regeneration times and convergence rates for Markov chains fn1 fn1 Work supported in part

R. Tweedie, G. Roberts, Richard L. Tweedie, 1999 .

Complexity Bounds for MCMC via Diffusion Limits

J. Rosenthal, G. Roberts, 2014, 1411.0712.

Convergence of Heavy‐tailed Monte Carlo Markov Chain Algorithms

G. Roberts, S. F. Jarner, S. Jarner, 2007 .

MCMC methods for diffusion bridges

G. Roberts, A. Stuart, J. Voss, 2008 .

On the efficiency of pseudo-marginal random walk Metropolis algorithms

J. Rosenthal, G. Roberts, C. Sherlock, 2013, The Annals of Statistics.

Adaptive Gibbs samplers and related MCMC methods

J. Rosenthal, G. Roberts, K. Latuszy'nski, 2011, 1101.5838.

Particle filters for partially observed diffusions

P. Fearnhead, G. Roberts, O. Papaspiliopoulos, 2007, 0710.4245.

Fast Langevin based algorithm for MCMC in high dimensions

G. Roberts, K. Zygalakis, Alain Durmus, 2015, 1507.02166.

Markov Chain Monte Carlo Methods

J. Rosenthal, G. Roberts, 2006 .

Surprising Convergence Properties of Some Simple Gibbs Samplers under Various Scans

J. Rosenthal, G. Roberts, 2015 .

Bayes factors for discrete observations from diffusion processes

Nicholas G. Polson, G. Roberts, 1994 .

Polynomial convergence rates of Markov chains.

G. Roberts, S. F. Jarner, S. Jarner, 2002 .

Robust Markov chain Monte Carlo Methods for Spatial Generalized Linear Mixed Models

Gareth O. Roberts, Martin Sköld, Ole F Christensen, 2006 .

Strategies for improving MCMC

Walter R. Gilks, Gareth O. Roberts, G. Roberts, 1995 .

Assessing Convergence of Markov Chain MonteCarlo

G. Roberts, AlgorithmsStephen P. Brooks, 1997 .

Bayesian Nonparametric Hidden Markov Models with application to the analysis of copy-number-variation in mammalian genomes.

C. Yau, G. Roberts, O. Papaspiliopoulos, 2011, Journal of the Royal Statistical Society. Series B, Statistical methodology.

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

J. Rosenthal, G. Roberts, 2006, math/0702412.

OPTIMAL SCALING FOR PARTIALLY UPDATING MCMC ALGORITHMS

G. Roberts, P. Neal, 2006, math/0607054.

Unbiased Monte Carlo: Posterior estimation for intractable/infinite-dimensional models

G. Roberts, S. Vollmer, S. Agapiou, 2014, Bernoulli.

Data Augmentation for Diffusions

G. Roberts, O. Papaspiliopoulos, O. Stramer, 2013 .

Complexity bounds for Markov chain Monte Carlo algorithms via diffusion limits

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2016, Journal of Applied Probability.

Optimal metropolis algorithms for product measures on the vertices of a hypercube

G. Roberts, 1998 .

Optimal Scaling of Random Walk Metropolis Algorithms with Non-Gaussian Proposals

G. Roberts, P. Neal, 2011 .

Optimal Scaling for Random Walk Metropolis on Spherically Constrained Target Densities

G. Roberts, P. Neal, 2008 .

Optimal scaling of random walk Metropolis algorithms with discontinuous target densities

W. K. Yuen, G. Roberts, P. Neal, 2012, 1210.5090.

Exact simulation of diffusions

G. Roberts, A. Beskos, 2005, math/0602523.

Retrospective exact simulation of diffusion sample paths with applications

G. Roberts, O. Papaspiliopoulos, A. Beskos, 2006 .

Bayesian non‐parametric hidden Markov models with applications in genomics

C. Yau, G. Roberts, C. Holmes, 2011 .

Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions

G. Roberts, Paolo Giudici, S. Brooks, 2003 .

Convergence of adaptive direction sampling

G. Roberts, W. Gilks, 1994 .

Minimising MCMC variance via diffusion limits, with an application to simulated tempering

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2014 .

OPTIMAL SCALING OF METROPOLIS-COUPLED MARKOV CHAIN

J. Rosenthal, G. Roberts, Y. Atchadé, 2009 .

Combinatorial identities associated with CFTP by

J. Rosenthal, G. Roberts, 2001 .

Exact Monte Carlo simulation of killed diffusions

G. Roberts, Bruno Casella, Bruno Casella, 2008, Advances in Applied Probability.

An MCMC method for diffusion bridges

G. Roberts, A. Stuart, J. Voss, 2008 .

Monte Carlo Maximum Likelihood Estimation for Discretely Observed Diffusion Processes

G. Roberts, O. Papaspiliopoulos, A. Beskos, 2009, 0903.0290.

Statistical inference and model selection for the 1861 Hagelloch measles epidemic.

G. Roberts, P. Neal, 2004, Biostatistics.

SUBGEOMETRIC ERGODICITY OF STRONG MARKOV PROCESSES

G. Roberts, G. Fort, 2005, math/0505260.

Nonparametric estimation of diffusions: a differential equations approach

G. Roberts, O. Papaspiliopoulos, A. Stuart, 2012 .

Markov chain Monte Carlo methods for switching diffusion models

G. Roberts, J. Liechty, 2001 .

Predicting undetected infections during the 2007 foot-and-mouth disease outbreak

G. Roberts, M. Keeling, C. Jewell, 2009, Journal of The Royal Society Interface.

A novel approach to real-time risk prediction for emerging infectious diseases: a case study in Avian Influenza H5N1.

G. Roberts, T. Kypraios, R. Christley, 2009, Preventive veterinary medicine.

Bayesian analysis for emerging infectious diseases

G. Roberts, T. Kypraios, P. Neal, 2009 .

General state space Markov chains and MCMC algorithms

J. Rosenthal, G. Roberts, 2004, math/0404033.

MEXIT: Maximal un-coupling times for stochastic processes

Philip A. Ernst, J. Rosenthal, G. Roberts, 2017, Stochastic Processes and their Applications.

An approximation scheme for quasi-stationary distributions of killed diffusions

Gareth O. Roberts, David Steinsaltz, G. Roberts, 2018, Stochastic Processes and their Applications.

MEXIT: Maximal un-coupling times for Markov processes

J. Rosenthal, G. Roberts, W. Kendall, 2017 .

Hitting Time and Convergence Rate Bounds for Symmetric Langevin Diffusions

J. Rosenthal, G. Roberts, 2016, 1611.03141.

Stability of noisy Metropolis –

G. Roberts, F. Medina-Aguayo, Hastings, 2015 .

Convergence of Conditional Metropolis-Hastings Samplers

Gareth O. Roberts, Jeffrey S. Rosenthal, Galin L. Jones, 2014, Advances in Applied Probability.

Stability of adversarial Markov chains, with an application to adaptive MCMC algorithms

Gareth O. Roberts, Neal Madras, Radu V. Craiu, 2014 .

A note on formal constructions of sequential conditional couplings

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2013 .

Quantitative Non-Geometric Convergence Bounds for Independence Samplers

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2011 .

From metropolis to diffusions: Gibbs states and optimal scaling

Gareth O. Roberts, L. A. Breyer, G. Roberts, 2000 .

Markov Chains and De‐initializing Processes

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2001 .

SMALL AND PSEUDO-SMALL SETS FOR MARKOV CHAINS

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2001 .

Markov Chains and De-initialising Processes by

J. Rosenthal, G. Roberts, 2001 .

Rates of convergence of stochastically monotone and continuous time Markov models

R. L. Tweedie, G. O. Roberts, R. Tweedie, 2000 .

A note on geometric ergodicity and floating-point roundoff error

Gareth O. Roberts, Jeffrey S. Rosenthal, L. A. Breyer, 2001 .

Convergence Properties of Perturbed Markov Chains

Gareth O. Roberts, Jeffrey S. Rosenthal, Peter O. Schwartz, 1998, Journal of Applied Probability.

Title : The random walk Metropolis : linking theory and practice through a case study Year of publication : 2009

P. Fearnhead, G. Roberts, C. Sherlock, 2009 .

Density Estimation for the Metropolis–Hastings Algorithm

Gareth O. Roberts, G. Roberts, M. Sköld, 2003 .

Recent progress on computable bounds and the simple slice sampler

G. Roberts, S. Rosenthal, 1999 .

Variance bounding Markov chains.

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2008 .

A Factorisation of Diffusion Measure and Finite Sample Path Constructions

Alexandros Beskos, Gareth O. Roberts, Omiros Papaspiliopoulos, 2008 .

Particle Filtering for Diffusions Avoiding Time-Discretisations

P. Fearnhead, G. Roberts, O. Papaspiliopoulos, 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

Quantitative Bounds for Convergence Rates of Continuous Time Markov Processes

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 1996 .

One-shot coupling for certain stochastic recursive sequences

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2002 .

An empirical study of the efficiency of EA for diffusion simulation

Gareth O. Roberts, Stefano Peluchetti, G. Roberts, 2008 .

Adaptive Markov Chain Monte Carlo through Regeneration

G. Roberts, W. Gilks, S. Sahu, 1998 .

Optimal Temperature Spacing for Regionally Weight-preserving Tempering

Nicholas G. Tawn, G. Roberts, 2018, 1810.05845.

Weak convergence and optimal scaling of random walk Metropolis algorithms

A. Gelman, G. Roberts, W. Gilks, 1997 .

J un 2 01 4 Systematic Physics Constrained Parameter Estimation of Stochastic Di ff erential Equations

G. Roberts, C. Franzke, D. Peavoy, 2014 .

Simulating bridges using confluent diffusions.

Paul A. Jenkins, G. Roberts, M. Sørensen, 2019, 1903.10184.

Perfect posterior simulation for mixture and hidden Markov models

L. A. Breyer, G. Roberts, K. K. Berthelsen, 2010 .

One-Shot CFTP; Application to a Class of Truncated Gaussian Densities

Alexandros Beskos, Gareth O. Roberts, G. Roberts, 2005 .

Dimension‐free mixing for high‐dimensional Bayesian variable selection

J. Rosenthal, G. Roberts, D. Vats, 2021, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Bayesian computation via the gibbs sampler and related markov chain monte carlo methods (with discus

Adrian F. M. Smith, G. Roberts, 1993 .

Combinatorial identifites associated with CFTP.

J. Rosenthal, G. Roberts, 2004 .

A note on convergence rates of Gibbs sampling for nonparametric mixtures.

J. Rosenthal, G. Roberts, Sonia Petrone, 1999 .

A quasi-ergodic theorem for evanescent processes

Gareth O. Roberts, L. A. Breyer, G. Roberts, 1999 .

Updating Schemes, Correlation Structure, Blocking and Parameterization for the Gibbs Sampler

G. Roberts, S. Sahu, Gareth O. Roberts, 1997 .

Latent diffusion models for survival analysis

Gareth O. Roberts, Laura M. Sangalli, G. Roberts, 2010, 1010.1688.

Geometric Ergodicity and Hybrid Markov Chains

J. Rosenthal, G. Roberts, 1997 .

Barker's algorithm for Bayesian inference with intractable likelihoods

Gareth O. Roberts, Flavio B. Gonccalves, Krzysztof Latuszy'nski, 2017 .

A note on geometri ergodi ityand oating-point roundo

J. Rosenthal, G. Roberts, errorbyLaird, 2000 .

The Zig-Zag process and super-efficient sampling for Bayesian analysis of big data

P. Fearnhead, G. Roberts, J. Bierkens, 2016, The Annals of Statistics.

Importance sampling techniques for estimation of diffusion models

Gareth O. Roberts, Omiros Papaspiliopoulos, G. Roberts, 2009 .

Filtering systems of coupled stochastic differential equations partially observed at high frequency

P. Fearnhead, G. Roberts, O. Papaspiliopoulos, 2007 .

Likelihood-based inference for correlated diffusions

Petros Dellaportas, Gareth O. Roberts, Konstantinos Kalogeropoulos, 2007 .

Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion)

P. Fearnhead, G. Roberts, O. Papaspiliopoulos, 2006 .

Contribution to the discussion of \Sequential Quasi-Monte-Carlo Sampling" { Gerber, Chopin

Gareth O. Roberts, Adam M. Johansen, Murray Pollock, 2014 .

Exact Simulation of Jump-Diffusion Processes with Monte Carlo Applications

Gareth O. Roberts, G. Roberts, Bruno Casella, 2011 .

A piecewise deterministic scaling limit of Lifted Metropolis-Hastings in the Curie-Weiss model

G. Roberts, J. Bierkens, 2015, 1509.00302.

Two convergence properties of hybrid samplers

J. Rosenthal, G. Roberts, 1998 .

Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms

R. Tweedie, G. Roberts, 1996 .

Corrigendum to : Bounds on regeneration times and convergence rates for Markov chains

Gareth O. Roberts, R. L. Tweedie, R. Tweedie, 2001 .

Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms

Adrian F. M. Smith, G. Roberts, 1994 .

The Acceptance Probability of the Hybrid Monte Carlo Method in High‐Dimensional Problems

Alexandros Beskos, Gareth O. Roberts, Andrew M. Stuart, 2010 .

MCMC Methods for Functions: ModifyingOld Algorithms to Make Them Faster

G. Roberts, A. Stuart, S. Cotter, 2012, 1202.0709.

J. Rosenthal, G. Roberts, Yanjia Bai, 1993 .

Corrigendum: Perfect slice samplers

G. Roberts, A. Mira, J. Møller, 2002 .

[Bayesian Computation and Stochastic Systems]: Comment

Walter R. Gilks, Gareth O. Roberts, Sujit K. Sahu, 1995 .

Optimal scaling of MCMC beyond Metropolis

Gareth O. Roberts, Dootika Vats, Sanket Agrawal, 2021, Advances in Applied Probability.

Markov chain Monte Carlo. A review article for section 10 (probability theory).

Gareth O. Roberts, G. Roberts, 2004 .

Stereographic Markov Chain Monte Carlo

G. Roberts, K. Latuszy'nski, Jun Yang, 2022, 2205.12112.

Skew brownian motion and complexity of the alps algorithm

Nicholas G. Tawn, J. Rosenthal, G. Roberts, 2020, Journal of Applied Probability.

A comparison theorem for conditioned Markov processes

G. Roberts, 1991, Journal of Applied Probability.

Adaptive Direction Sampling

Walter R. Gilks, Gareth O. Roberts, Edward I. George, 1994 .

General state space Markov chains and MCMC algorithms

J. Rosenthal, G. Roberts, 2004, math/0404033.

Inference for diffusion models using time change transformations

G. Roberts, P. Dellaportas, K. Kalogeropoulos, 2011 .

Hitting Time and Convergence Rate Bounds for Symmetric Langevin Diffusions

J. Rosenthal, G. Roberts, 2016, Methodology and Computing in Applied Probability.

General state space Markov chains and MCMC algorithms

J. Rosenthal, G. Roberts, 2004, math/0404033.

A note on acceptance rate criteria for CLTS for Metropolis–Hastings algorithms

G. Roberts, 1999, Journal of Applied Probability.

Football Group Draw Probabilities and Corrections

J. Rosenthal, G. Roberts, 2022, 2205.06578.

An epidemic model for an evolving pathogen with strain-dependent immunity.

G. Roberts, S. Spencer, Adam Griffin, 2020, Mathematical biosciences.

Sampling using Adaptive Regenerative Processes

C. Robert, G. Roberts, M. Pollock, 2022, 2210.09901.

Scaling limits for the transient phase of local Metropolis–Hastings algorithms

J. Rosenthal, G. Roberts, O. F. Christensen, 2005 .

Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms

J. Rosenthal, G. Roberts, 2007, Journal of Applied Probability.

Optimal tuning of the hybrid Monte Carlo algorithm

J. M. Sanz-Serna, N. Pillai, G. Roberts, 2010, 1001.4460.

Simulation from quasi-stationary distributions on reducible state spaces

G. Roberts, S. Spencer, P. Jenkins, 2016, Advances in Applied Probability.

Stability of the Gibbs sampler for Bayesian hierarchical models

G. Roberts, O. Papaspiliopoulos, 2007, 0710.4234.

Hitting Time and Convergence Rate Bounds for Symmetric Langevin Diffusions

J. Rosenthal, G. Roberts, 2017, Methodology and Computing in Applied Probability.

CLTs and Asymptotic Variance of Time-Sampled Markov Chains

G. Roberts, K. Łatuszyński, 2011, Methodology and Computing in Applied Probability.

Exact Simulation Problems for Jump-Diffusions

F. B. Gonçalves, G. Roberts, 2013, Methodology and Computing in Applied Probability.

We consider the non-explosivity of birth and death processes conditioned not to become extinct. As is often the case with problems involving weak convergence of

G. Roberts, P. Pollett, S. Jacka, 1997 .

The Lifebelt Particle Filter for robust estimation from low-valued count data

G. Roberts, A. Presanis, D. Angelis, 2022, 2212.04400.

Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets

G. Roberts, C. Sherlock, 2009, 0909.0856.

Efficient construction of reversible jump Markov chain Monte Carlo proposal distributions

G. Roberts, Paolo Giudici, S. Brooks, 2003 .

The Scalable Langevin Exact Algorithm : Bayesian Inference for Big Data

P. Fearnhead, A. M. Johansen, G. Roberts, 2016 .

Quasi‐stationary Monte Carlo and the ScaLE algorithm

Paul Fearnhead, Adam M. Johansen, Gareth O. Roberts, 2016, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

CLTs and Asymptotic Variance of Time-Sampled Markov Chains

G. Roberts, K. Łatuszyński, 2011, 1102.2171.

Optimal scaling of the random walk Metropolis on unimodal elliptically symmetric targets.

G. Roberts, C. Sherlock, 2009 .

Weak convergence of conditioned birth and death processes

G. Roberts, S. Jacka, 1994, Journal of Applied Probability.

Networks and the Epidemiology of Infectious Disease

Matt J. Keeling, Gareth O. Roberts, Thomas House, 2010, Interdisciplinary perspectives on infectious diseases.

A case study in non-centering for data augmentation: Stochastic epidemics

PETER NEAL, GARETH ROBERTS, G. Roberts, 2005, Stat. Comput..

Obituary: Richard Lewis Tweedie

Sean P. Meyn, G. Roberts, W. Dunsmuir, 2002, Journal of Applied Probability.

Multilevel Linear Models, Gibbs Samplers and Multigrid Decompositions

Gareth Roberts, Giacomo Zanella, G. Roberts, 2017, Bayesian Analysis.

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

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 1998 .

High-dimensional scaling limits of piecewise deterministic sampling algorithms

Gareth O. Roberts, Joris Bierkens, Kengo Kamatani, 2018, The Annals of Applied Probability.

Ergodicity of the zigzag process

G. Roberts, P. Zitt, J. Bierkens, 2017, The Annals of Applied Probability.

Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo

Paul Fearnhead, Joris Bierkens, Murray Pollock, 2016, Statistical Science.

Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains

P. Fearnhead, A. Doucet, G. Roberts, 2017, 1701.04244.

Systematic physics constrained parameter estimation of stochastic differential equations

Gareth O. Roberts, Christian L. E. Franzke, Daniel Peavoy, 2013, Comput. Stat. Data Anal..

Automatic Zig-Zag sampling in practice

G. Roberts, S. Spencer, Alice Corbella, 2022, Statistics and Computing.

A dynamic model of bovine tuberculosis spread and control in Great Britain

Ellen Brooks-Pollock, Gareth O. Roberts, Matt J. Keeling, 2014, Nature.

The Random Walk Metropolis: Linking Theory and Practice Through a Case Study

P. Fearnhead, G. Roberts, C. Sherlock, 2010, 1011.6217.

On convergence of the EM algorithmand the Gibbs sampler

Gareth O. Roberts, Sujit K. Sahu, G. Roberts, 1999, Stat. Comput..

Assessing delivery practices of mothers over time and over space in Uganda, 2003–2012

G. Roberts, T. House, S. Lwanga, 2016, Emerging Themes in Epidemiology.

Random-weight particle filtering of continuous time

Andrew M. Stuart, Paul Fearnhead, Gareth O. Roberts, 2010 .

A utility based approach to information for stochastic differential equations

Nicholas G. Polson, G. Roberts, 1993 .

Markov Chain Monte Carlo for Exact Inference for Diffusions

Paul Fearnhead, Gareth O. Roberts, Omiros Papaspiliopoulos, 2011, 1102.5541.

Convergence assessment techniques for Markov chain Monte Carlo

Gareth O. Roberts, Stephen P. Brooks, G. Roberts, 1998, Stat. Comput..

Bayesian Fusion: Scalable unification of distributed statistical analyses

Hongsheng Dai, Murray Pollock, Gareth Roberts, 2021, 2102.02123.

On the exact and ε-strong simulation of (jump) diffusions

Gareth O. Roberts, Adam M. Johansen, Murray Pollock, 2013, 1302.6964.

Link analysis ranking: algorithms, theory, and experiments

Allan Borodin, Panayiotis Tsaparas, Gareth O. Roberts, 2005, TOIT.

Rao–Blackwellisation in the Markov Chain Monte Carlo Era

C. Robert, G. Roberts, 2021, International Statistical Review.

Scalable importance tempering and Bayesian variable selection

Gareth Roberts, Giacomo Zanella, G. Roberts, 2018, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Scaling of Piecewise Deterministic Monte Carlo for Anisotropic Targets

G. Roberts, K. Kamatani, J. Bierkens, 2023, 2305.00694.

Asymptotic analysis of the random walk Metropolis algorithm on ridged densities

Alexandros Beskos, Natesh Pillai, Alexandre Thiery, 2015, The Annals of Applied Probability.

Exact Simulation Problems for Jump-Diffusions

Gareth O. Roberts, F. B. Gonçalves, Flávio B. Gonçalves, 2014 .

Stability of noisy Metropolis–Hastings

Anthony Lee, F. J. Medina-Aguayo, Gareth O. Roberts, 2015, Stat. Comput..

Rao-Blackwellization in the MCMC era

C. Robert, G. Roberts, 2021, 2101.01011.

Optimal Scaling Results for a Wide Class of Proximal MALA Algorithms

G. Roberts, Alain Durmus, F. R. Crucinio, 2023, 2301.02446.

An adaptive approach to Langevin MCMC

G. Roberts, Tristan Marshall, 2012, Stat. Comput..

Air Markov Chain Monte Carlo

Gareth Roberts, Krzysztof Latuszynski, Cyril Chimisov, 2018, 1801.09309.

Regeneration-enriched Markov processes with application to Monte Carlo

G. Roberts, D. Steinsaltz, M. Pollock, 2019, The Annals of Applied Probability.

Polynomial Convergence Rates of Piecewise Deterministic Markov Processes

J. Rosenthal, G. Roberts, 2023, Methodology and Computing in Applied Probability.

Bayesian inference for high-dimensional discrete-time epidemic models: spatial dynamics of the UK COVID-19 outbreak (preprint)

G. Roberts, B. Rowlingson, J. Read, 2023, 2306.07987.

Efficient real-time monitoring of an emerging influenza epidemic: how feasible?

Lorenz Wernisch, Paul J. Birrell, Gareth O Roberts, 2016, 1608.05292.

Some boundary hitting problems for diffusion processes

Gareth O. Roberts, G. Roberts, 1988 .

On strong forms of weak convergence

Gareth O. Roberts, Saul D. Jacka, G. Roberts, 1997 .

Analysis of the Gibbs Sampler for Gaussian hierarchical models via multigrid decomposition

G. Roberts, Giacomo Zanella, 2017, 1703.06098.

Towards optimal scaling of metropolis-coupled Markov chain Monte Carlo

Gareth O. Roberts, Jeffrey S. Rosenthal, Yves F. Atchadé, 2011, Stat. Comput..

Football Group Draw Probabilities and Corrections

J. Rosenthal, G. Roberts, 2022, 2205.06578.

Inference for stochastic volatility model using time change transformations

Konstantinos Kalogeropoulos, Gareth Roberts, Petros Dellaportas, 2007, 0711.1594.

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

Allan Borodin, Panayiotis Tsaparas, Gareth O. Roberts, 2001, WWW '01.

Approximate Predetermined Convergence Properties of the Gibbs Sampler

G. Roberts, S. Sahu, 2001 .

Weak convergence of conditioned processes on a countable state space

G. O. Roberts, G. Roberts, S. Jacka, 1995, Journal of Applied Probability.

Non parametric Bayesian drift estimation for one-dimensional diffusion processes

G. Roberts, O. Papaspiliopoulos, A. Stuart, 2009 .

Geometric Ergodicity and Hybrid Markov Chains

J. Rosenthal, G. Roberts, 1997 .

Inverse method of images

G. Roberts, H. Daniels, Violet S.F. Lo, 2002 .

Erratum to: Extremal indices, geometric ergodicity of Markov chains, and MCMC

J. Rosenthal, G. Roberts, J. Segers, 2010 .

Extremal indices, geometric ergodicity of Markov chains, and MCMC

Johan Segers, Gareth O. Roberts, Jeffrey S. Rosenthal, 2006 .

Monte Carlo : posterior estimation for intractable / infinite-dimensional models

G. Roberts, S. Vollmer, S. Agapiou, 2016 .

A Study of the Efficiency of Exact Methods for Diffusion Simulation

G. Roberts, Stefano Peluchetti, 2012 .

Optimal scaling of random-walk metropolis algorithms on general target distributions

J. Rosenthal, G. Roberts, Jun Yang, 2019, Stochastic Processes and their Applications.

Monte Carlo simulation of killed diffusions with a single barrier

Gareth O. Roberts, Bruno Casella, G. Roberts, 2006, valuetools '06.

Speed Up Zig-Zag

G. Roberts, G. Mahan, Giorgos Vasdekis, 2021, 2103.16620.

Scalable inference for crossed random effects models

Gareth O. Roberts, Omiros Papaspiliopoulos, Giacomo Zanella, 2018, Biometrika.

Flexible Bayesian Inference for Diffusion Processesusing Splines

G. Roberts, M. Pollock, P. A. Jenkins, 2021, Methodology and Computing in Applied Probability.

Optimal scalings for local Metropolis--Hastings chains on nonproduct targets in high dimensions

Gareth Roberts, Alexandros Beskos, Andrew Stuart, 2009, 0908.0865.

Theoretical properties of quasi-stationary Monte Carlo methods

G. Roberts, D. Steinsaltz, Martin Kolb, 2017, The Annals of Applied Probability.

Possible biases induced by MCMC convergence diagnosticsbyMary

J. Rosenthal, G. Roberts, K. Cowles, 1997 .

Optimal scalings of Metropolis-Hastings algorithms for non-product targets in high dimensions

G. Roberts, A. Stuart, Alexandros Baskos, 2009 .

Continious-time Importance Sampling: Monte Carlo Methods which Avoid Time-discretisation Error

Paul Fearnhead, Gareth O. Roberts, Giorgos Sermaidis, 2017, 1712.06201.

Efficient Real-Time Monitoring of an Emerging Influenza Pandemic: How Feasible?

Leonhard Held, Lorenz Wernisch, Daniela De Angelis, 2020, The annals of applied statistics.

Exact Monte Carlo likelihood-based inference for jump-diffusion processes

Gareth O. Roberts, Krzysztof G. Latuszy'nski, Fl'avio B. Gonccalves, 2017, Journal of the Royal Statistical Society Series B: Statistical Methodology.

Stability of Partially Implicit Langevin Schemes and Their MCMC Variants

Gareth O. Roberts, Bruno Casella, Osnat Stramer, 2011 .

An Introduction to MCMC

Petros Dellaportas, Gareth O. Roberts, G. Roberts, 2003 .

Geometric L 2 and L 1 convergence are equivalent for reversible Markov chains

R. Tweedie, G. Roberts, 2001, Journal of Applied Probability.

Adapting the Gibbs sampler

Gareth Roberts, Krzysztof Latuszynski, Cyril Chimisov, 2018, 1801.09299.

Geometric Ergodicity and Hybrid Markov Chains

J. Rosenthal, G. Roberts, 1997 .

ε-Strong simulation of the Brownian path

Alexandros Beskos, Gareth Roberts, Stefano Peluchetti, 2011, 1110.0110.

Tempered Langevin diffusions and algorithms

G. Roberts, O. Stramer, 2002 .

Weight-preserving simulated tempering

Gareth O. Roberts, Jeffrey S. Rosenthal, Nicholas G. Tawn, 2018, Statistics and Computing.

Quasi‐stationary Monte Carlo and the ScaLE algorithm

P. Fearnhead, A. M. Johansen, G. Roberts, 2016, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Optimal Scaling of Metropolis Algorithms on General Target Distributions

J. Rosenthal, G. Roberts, Jun Yang, 2019 .

The Hazard Rate Tangent Approximation for Boundary Hitting Times

G. Roberts, C. F. Shortland, 1995 .

Convergence of Conditional Metropolis-Hastings Samplers , with an Application to Inference for Discretely-Observed Diffusions

Galin L. Jones, J. Rosenthal, G. Roberts, 2012 .

Downweighting tightly knit communities in world wide web ranking.

Gareth O. Roberts, Jeffrey S. Rosenthal, J. Rosenthal, 2003 .