Citation: O. Roberts, Gareth et S. Rosenthal, Jeffrey, Minimising MCMC variance via diffusion limits, with an application to simulated tempering, Annals of applied probability , 24(1), 2014, pp. 131-149
Citation: F. Jarner, Søren et O. Roberts, Gareth, Polynomial Convergence Rates of Markov Chains, Annals of applied probability , 12(1), 2002, pp. 224-247
Citation: J. Neal, Peter et O. Roberts, Gareth, Statistical inference and model selection for the 1861 Hagelloch measles epidemic, Biostatistics (Oxford. Print) , 5(2), 2004, pp. 249-261
Citation: Casella, Bruno et O. Roberts, Gareth, Exact Monte Carlo simulation of killed diffusions, Advances in applied probability , 40(1), 2008, pp. 273-291
Citation: O. Roberts, Gareth et S. Rosenthal, Jeffrey, Two convergence properties of hybrid samplers, Annals of applied probability , 8(2), 1998, pp. 397-407
Citation: P. Jewell, Chris et O. Roberts, Gareth, Enhancing Bayesian risk prediction for epidemics using contact tracing, Biostatistics (Oxford. Print) , 13(4), 2012, pp. 567-579
Citation: Andrieu, Christophe et O. Roberts, Gareth, The pseudo-marginal approach for efficient Monte Carlo computations, Annals of statistics , 37(2), 2009, pp. 697-725
Authors:
Kalogeropoulos, Konstantinos
Roberts, Gareth O.
Dellaportas, Petros
Citation: Kalogeropoulos, Konstantinos et al., Inference for stochastic volatility models using time change transformations, Annals of statistics , 38(2), 2010, pp. 784-807
Citation: O. Roberts, Gareth et S. Rosenthal, Jeffrey, On convergence rates of Gibbs samplers for uniform distributions, Annals of applied probability , 8(4), 1998, pp. 1291-1302
Citation: O. Roberts, Gareth et S. Rosenthal, Jeffrey, Harris recurrence of Metropolis-within-Gibbs and trans-dimensional Markov chains, Annals of applied probability , 16(4), 2006, pp. 2123-2139
Authors:
Craiu, Radu V.
Gray, Lawrence
.atuszy.ski, Krzysztof
Madras, Neal
Roberts, Gareth O.
Rosenthal, Jeffrey S.
Citation: V. Craiu, Radu et al., Stability of adversarial Markov chains, with an application to adaptive MCMC algorithms, Annals of applied probability , 25(6), 2015, pp. 3592-3623