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Volumn 43, Issue 1, 2015, Pages 238-275

On the efficiency of pseudo-marginal random walk metropolis algorithms

Author keywords

Diffusion limit; Markov chain monte carlo; MCMC; Optimal scaling; Particle methods; Pseudo marginal random walk metropolis

Indexed keywords


EID: 84922540951     PISSN: 00905364     EISSN: 21688966     Source Type: Journal    
DOI: 10.1214/14-AOS1278     Document Type: Article
Times cited : (148)

References (35)
  • 2
    • 60149103563 scopus 로고    scopus 로고
    • The pseudo-marginal approach for efficient Monte Carlo computations
    • MR2502648
    • ANDRIEU, C. and ROBERTS, G. O. (2009). The pseudo-marginal approach for efficient Monte Carlo computations. Ann. Statist. 37 697-725. MR2502648
    • (2009) Ann. Statist. , vol.37 , pp. 697-725
    • Andrieu, C.1    Roberts, G.O.2
  • 4
    • 0043210659 scopus 로고    scopus 로고
    • Estimation of population growth or decline in genetically monitored populations
    • BEAUMONT, M. A. (2003). Estimation of population growth or decline in genetically monitored populations. Genetics 164 1139-1160.
    • (2003) Genetics , vol.164 , pp. 1139-1160
    • Beaumont, M.A.1
  • 5
    • 42649097792 scopus 로고    scopus 로고
    • Weak convergence of Metropolis algorithms for non-i.I.D. Target distributions
    • MR2344305
    • B ÉDARD, M. (2007).Weak convergence of Metropolis algorithms for non-i.i.d. target distributions. Ann. Appl. Probab. 17 1222-1244. MR2344305
    • (2007) Ann. Appl. Probab. , vol.17 , pp. 1222-1244
    • Bédard, M.1
  • 6
    • 59149089296 scopus 로고    scopus 로고
    • Optimal scaling ofMetropolis algorithms: Heading toward general target distributions
    • MR2532248
    • B ÉDARD, M. and ROSENTHAL, J. S. (2008). Optimal scaling ofMetropolis algorithms: Heading toward general target distributions. Canad. J. Statist. 36 483-503. MR2532248
    • (2008) Canad. J. Statist. , vol.36 , pp. 483-503
    • Bédard, M.1    Rosenthal, J.S.2
  • 8
    • 69149086344 scopus 로고    scopus 로고
    • Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions
    • MR2537193
    • BESKOS, A., ROBERTS, G. and STUART, A. (2009). Optimal scalings for local Metropolis-Hastings chains on nonproduct targets in high dimensions. Ann. Appl. Probab. 19 863-898. MR2537193
    • (2009) Ann. Appl. Probab. , vol.19 , pp. 863-898
    • Beskos, A.1    Roberts, G.2    Stuart, A.3
  • 9
    • 26844542053 scopus 로고    scopus 로고
    • Optimal scaling of MaLa for nonlinear regression
    • MR2071431
    • BREYER, L. A., PICCIONI, M. and SCARLATTI, S. (2004). Optimal scaling of MaLa for nonlinear regression. Ann. Appl. Probab. 14 1479-1505. MR2071431
    • (2004) Ann. Appl. Probab. , vol.14 , pp. 1479-1505
    • Breyer, L.A.1    Piccioni, M.2    Scarlatti, S.3
  • 10
    • 0041906743 scopus 로고    scopus 로고
    • From Metropolis to diffusions: Gibbs states and optimal scaling
    • MR1794535
    • BREYER, L. A. and ROBERTS, G. O. (2000). From Metropolis to diffusions: Gibbs states and optimal scaling. Stochastic Process. Appl. 90 181-206. MR1794535
    • (2000) Stochastic Process. Appl. , vol.90 , pp. 181-206
    • Breyer, L.A.1    Roberts, G.O.2
  • 12
    • 0042958264 scopus 로고    scopus 로고
    • The penalty method for random walks with uncertain energies
    • CEPERLEY, D. M. and DEWING, M. (1999). The penalty method for random walks with uncertain energies. The Journal of Chemical Physics 110 9812.
    • (1999) The Journal of Chemical Physics , vol.110 , pp. 9812
    • Ceperley, D.M.1    Dewing, M.2
  • 17
    • 84860901236 scopus 로고    scopus 로고
    • Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo
    • GOLIGHTLY, A. andW ILKINSON, D. J. (2011). Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo. Interface Focus 1 807-820.
    • (2011) Interface Focus , vol.1 , pp. 807-820
    • Golightly, A.1    Wilkinson, D.J.2
  • 18
    • 0027580559 scopus 로고
    • Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing
    • GORDON, N. J., SALMOND, D. J. and SMITH, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. Radar and Signal Processing, IEE Proceedings F 140 107-113.
    • (1993) IEE Proceedings F , vol.140 , pp. 107-113
    • Gordon, N.J.1    Salmond, D.J.2    Smith, A.F.M.3
  • 19
    • 84860249785 scopus 로고    scopus 로고
    • Fitting complex population models by combining particle filters with Markov chain Monte Carlo
    • KNAPE, J. and DE VALPINE, P. (2012). Fitting complex population models by combining particle filters with Markov chain Monte Carlo. Ecology 93 256-263.
    • (2012) Ecology , vol.93 , pp. 256-263
    • Knape, J.1    De Valpine, P.2
  • 20
    • 0347361674 scopus 로고    scopus 로고
    • Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data
    • L I, N. and STEPHENS, M. (2003). Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. Genetics 165 2213-2233.
    • (2003) Genetics , vol.165 , pp. 2213-2233
    • Stephens, M.1
  • 22
    • 77949374261 scopus 로고    scopus 로고
    • Adaptively scaling the Metropolis algorithm using expected squared jumped distance
    • MR2640698
    • PASARICA, C. and GELMAN, A. (2010). Adaptively scaling the Metropolis algorithm using expected squared jumped distance. Statist. Sinica 20 343-364. MR2640698
    • (2010) Statist. Sinica , vol.20 , pp. 343-364
    • Pasarica, C.1    Gelman, A.2
  • 23
    • 84879614507 scopus 로고    scopus 로고
    • Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions
    • MR3024970
    • PILLAI, N. S., STUART, A. M. and T HIÉRY, A. H. (2012). Optimal scaling and diffusion limits for the Langevin algorithm in high dimensions. Ann. Appl. Probab. 22 2320-2356. MR3024970
    • (2012) Ann. Appl. Probab. , vol.22 , pp. 2320-2356
    • Pillai, N.S.1    Stuart, A.M.2    Thiéry, A.H.3
  • 24
    • 84868208864 scopus 로고    scopus 로고
    • On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
    • MR2991856
    • PITT, M. K., SILVA, R. D . S., GIORDANI, P. and KOHN, R. (2012). On some properties of Markov chain Monte Carlo simulation methods based on the particle filter. J. Econometrics 171 134-151. MR2991856
    • (2012) J. Econometrics , vol.171 , pp. 134-151
    • Pitt, M.K.1    Silva, R.D.S.2    Giordani, P.3    Kohn, R.4
  • 25
    • 79952176832 scopus 로고    scopus 로고
    • Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
    • MR2804210
    • POYIADJIS, G., DOUCET, A. and SINGH, S. S. (2011). Particle approximations of the score and observed information matrix in state space models with application to parameter estimation. Biometrika 98 65-80. MR2804210
    • (2011) Biometrika , vol.98 , pp. 65-80
    • Poyiadjis, G.1    Doucet, A.2    Singh, S.S.3
  • 26
    • 0031285157 scopus 로고    scopus 로고
    • Weak convergence and optimal scaling of random walk Metropolis algorithms
    • MR1428751
    • ROBERTS, G. O., GELMAN, A. and GILKS, W. R. (1997). Weak convergence and optimal scaling of random walk Metropolis algorithms. Ann. Appl. Probab. 7 110-120. MR1428751
    • (1997) Ann. Appl. Probab. , vol.7 , pp. 110-120
    • Roberts, G.O.1    Gelman, A.2    Gilks, W.R.3
  • 27
    • 0000936678 scopus 로고    scopus 로고
    • Optimal scaling of discrete approximations to Langevin diffusions
    • MR1625691
    • ROBERTS, G. O. and ROSENTHAL, J. S. (1998). Optimal scaling of discrete approximations to Langevin diffusions. J. R. Stat. Soc. Ser. B Stat. Methodol. 60 255-268. MR1625691
    • (1998) J. R. Stat. Soc. Ser. B Stat. Methodol. , vol.60 , pp. 255-268
    • Roberts, G.O.1    Rosenthal, J.S.2
  • 28
    • 0013037129 scopus 로고    scopus 로고
    • Optimal scaling for various Metropolis-Hastings algorithms. Statist
    • MR1888450
    • ROBERTS, G. O. and ROSENTHAL, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statist. Sci. 16 351-367. MR1888450
    • (2001) Sci. , vol.16 , pp. 351-367
    • Roberts, G.O.1    Rosenthal, J.S.2
  • 29
    • 84892416832 scopus 로고    scopus 로고
    • Minimising MCMC variance via diffusion limits, with an application to simulated tempering
    • MR3161644
    • ROBERTS, G. O. and ROSENTHAL, J. S. (2014). Minimising MCMC variance via diffusion limits, with an application to simulated tempering. Ann. Appl. Probab. 24 131-149. MR3161644
    • (2014) Ann. Appl. Probab. , vol.24 , pp. 131-149
    • Roberts, G.O.1    Rosenthal, J.S.2
  • 31
    • 84879076994 scopus 로고    scopus 로고
    • Optimal scaling of the random walk Metropolis: General criteria for the 0.234 acceptance rule
    • MR3076768
    • SHERLOCK, C. (2013). Optimal scaling of the random walk Metropolis: General criteria for the 0.234 acceptance rule. J. Appl. Probab. 50 1-15. MR3076768
    • (2013) J. Appl. Probab. , vol.50 , pp. 1-15
    • Sherlock, C.1
  • 32
    • 78650237279 scopus 로고    scopus 로고
    • The random walk Metropolis: Linking theory and practice through a case study
    • MR2789988
    • SHERLOCK, C., FEARNHEAD, P. and ROBERTS, G. O. (2010). The random walk Metropolis: Linking theory and practice through a case study. Statist. Sci. 25 172-190. MR2789988
    • (2010) Statist. Sci. , vol.25 , pp. 172-190
    • Sherlock, C.1    Fearnhead, P.2    Roberts, G.O.3
  • 33
    • 72249090639 scopus 로고    scopus 로고
    • Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets
    • MR2555199
    • SHERLOCK, C. and ROBERTS, G. (2009). Optimal scaling of the random walk Metropolis on elliptically symmetric unimodal targets. Bernoulli 15 774-798. MR2555199
    • (2009) Bernoulli , vol.15 , pp. 774-798
    • Sherlock, C.1    Roberts, G.2
  • 34
    • 0003053548 scopus 로고
    • Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods (with discussion)
    • MR1210421
    • SMITH, A. F. M. and ROBERTS, G. O. (1993). Bayesian computation via the Gibbs sampler and related Markov chain Monte Carlo methods (with discussion). J. R. Stat. Soc. Ser. B Stat. Methodol. 55 3-23. MR1210421
    • (1993) J. R. Stat. Soc. Ser. B Stat. Methodol. , vol.55 , pp. 3-23
    • Smith, A.F.M.1    Roberts, G.O.2
  • 35
    • 0000576595 scopus 로고
    • Markov chains for exploring posterior distributions
    • MR1329166
    • TIERNEY, L. (1994). Markov chains for exploring posterior distributions. Ann. Statist. 22 1701-1762. MR1329166
    • (1994) Ann. Statist. , vol.22 , pp. 1701-1762
    • Tierney, L.1


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