| Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods AFM Smith, GO Roberts Journal of the Royal Statistical Society: Series B (Methodological) 55 (1), 3-23, 1993 | 2212 | 1993 |
| Weak convergence and optimal scaling of random walk Metropolis algorithms GO Roberts, A Gelman, WR Gilks The annals of applied probability 7 (1), 110-120, 1997 | 1781 | 1997 |
| Efficient metropolis jumping rules A Gelman, G Roberts, W Gilks Bayesian statistics 5, 599-608, 1996 | 1298 | 1996 |
| Optimal scaling for various Metropolis-Hastings algorithms GO Roberts, JS Rosenthal Statistical science 16 (4), 351-367, 2001 | 1150 | 2001 |
| Examples of adaptive MCMC GO Roberts, JS Rosenthal Journal of Computational and Graphical Statistics 18 (2), 349-367, 2009 | 946 | 2009 |
| The EM algorithm—an old folk‐song sung to a fast new tune XL Meng, D Van Dyk Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 1997 | 864 | 1997 |
| Exponential convergence of Langevin distributions and their discrete approximations GO Roberts, RL Tweedie Bernoulli 2 (4), 341-363, 1996 | 791 | 1996 |
| The pseudo-marginal approach for efficient Monte Carlo computations C Andrieu, GO Roberts The Annals of Statistics 37 (2), 697-725, 2009 | 760 | 2009 |
| General state space Markov chains and MCMC algorithms GO Roberts, JS Rosenthal Probability surveys 1, 20-71, 2004 | 740 | 2004 |
| Optimal scaling of discrete approximations to Langevin diffusions GO Roberts, JS Rosenthal Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 1998 | 555 | 1998 |
| Updating schemes, correlation structure, blocking and parameterization for the Gibbs sampler GO Roberts, SK Sahu Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 1997 | 518 | 1997 |
| Markov chain concepts related to sampling algorithms GO Roberts Markov chain Monte Carlo in practice 57, 45-58, 1996 | 510 | 1996 |
| Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms GO Roberts, RL Tweedie Biometrika 83 (1), 95-110, 1996 | 471 | 1996 |
| Simple conditions for the convergence of the Gibbs sampler and Metropolis-Hastings algorithms GO Roberts, AFM Smith Stochastic processes and their applications 49 (2), 207-216, 1994 | 450 | 1994 |
| Coupling and ergodicity of adaptive Markov chain Monte Carlo algorithms GO Roberts, JS Rosenthal Journal of applied probability 44 (2), 458-475, 2007 | 449 | 2007 |
| Exact and computationally efficient likelihood‐based estimation for discretely observed diffusion processes (with discussion) A Beskos, O Papaspiliopoulos, GO Roberts, P Fearnhead Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2006 | 415 | 2006 |
| MCMC methods for functions: modifying old algorithms to make them faster SL Cotter, GO Roberts, AM Stuart, D White Statistical Science, 424-446, 2013 | 414 | 2013 |
| Assessing convergence of Markov chain Monte Carlo algorithms SP Brooks, GO Roberts Statistics and Computing 8 (4), 319-335, 1998 | 409 | 1998 |
| Convergence assessment techniques for Markov chain Monte Carlo SP Brooks, GO Roberts Statistics and Computing 8 (4), 319-335, 1998 | 399 | 1998 |
| Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models O Papaspiliopoulos, GO Roberts Biometrika 95 (1), 169-186, 2008 | 393 | 2008 |
JM Sanz-SernaUniversidad Carlos III de MadridVerified email at math.uc3m.es
JM Sanz-SernaUniversidad Carlos III de MadridVerified email at math.uc3m.es