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The pseudo-marginal (PM) approach is increasingly used for Bayesian inference in statistical models, where the likelihood is intractable but can be estimated unbiasedly.
The skin cancer prevention study: design of a clinical trial of beta-carotene among persons at high risk for nonmelanoma skin cancer
Greenberg, E. R., Baron, J. A., Stevens, M. M., Stukel, T. A., Mandel, J. S., Spencer, S. K., Elias, P. M., Lowe, N., Nierenberg, D. N., G., B., and Vance, J. C. (1989) · 1989
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Random Number Generation and Quasi-Monte Carlo Methods
Niederreiter, H. (1992) · 1992
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Statistics and Econometric Models
Gourieroux, C. and Monfort, A. (1995) · 1995
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Monte Carlo maximum likelihood estimation for non-Gaussian state space models
Durbin, J. and Koopman, S. J. (1997) · 1997
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Scrambled net variance for integrals of smooth functions
Owen, A. B. (1997) · 1997
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Likelihood analysis of non-Gaussian measurement time series
Shephard, N. and Pitt, M. K. (1997) · 1997
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Inferring coalescence times from DNA sequence data
Tavare, S., Balding, D. J., Griffiths, R. C., and Donnelly, P. (1997) · 1997
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On the l2-discrepancy for anchored boxes
Matousek, J. (1998) · 1998
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Vaart, A. W. (1998) · 1998
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A noisy Monte Carlo algorithm
Lin, L., Liu, K., and Sloan, J. (2000) · 2000
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Numerical techniques for maximum likelihood estimation of continuous-time diffusion processes
Durham, G. B. and Gallant, A. R. (2002) · 2002
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Estimation of population growth or decline in genetically monitored populations
Beaumont, M. A. (2003) · 2003
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On the asymptotic distribution of scrambled net quadrature
Loh, W.-L. (2003) · 2003
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Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications
Del Moral, P. (2004) · 2004
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Stable Distributions: Models for Heavy-Tailed Data
Nolan, J. (2007) · 2007
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The pseudo-marginal approach for efficient Monte Carlo computations
Andrieu, C. and Roberts, G. (2009) · 2009
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Particle Markov chain Monte Carlo methods
Andrieu, C., Doucet, A., and Holenstein, R. (2010) · 2010
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Digital nets and sequence. Discrepancy theory and quasi-Monte Carlo integration
Dick, J. and Pillichshammer, F. (2010) · 2010
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Latent Markov Models for Longitudinal Data
Bartolucci, F., Farcomeni, A., and Pennoni, F. (2012) · 2012
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Likelihood-free Bayesian inference for α \alpha -stable models
Peters, G., Sisson, S., and Fan, Y. (2012) · 2012
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On some properties of Markov chain Monte Carlo simulation methods based on the particle filter
Pitt, M. K., Silva, R. S., Giordani, P., and Kohn, R. (2012) · 2012
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Component-wise Markov chain Monte Carlo: Uniform and geometric ergodicity under mixing and composition
Johnson, A. A., Jones, G. L., and Neath, R. C. (2013) · 2013
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Accelerating pseudo-marginal Metropolis-Hastings by correlating auxiliary variables
Dahlin, J., Lindsten, F., Kronander, J., and Schön, T. B. (2015) · 2015
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Discussion on particle Markov chain Monte Carlo methods
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Adaptively scaling the Metropolis algorithm using expected squared jumped distance
Pasarica, C. and Gelman, A. (2010) · 2010
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Conditional Akaike information under generalized linear and proportional hazards mixed models
Donohue, M. C., Overholser, R., Xu, R., and Vaida, F. (2011) · 2011
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Applied Longitudinal Analysis
Fitzmaurice, G. M., Laird, N. M., and Ware, J. H. (2011) · 2011
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Bayesian inference based only on simulated likelihood: Particle filter analysis of dynamic economic models
Flury, T. and Shephard, N. (2011) · 2011
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Bayesian inference for irreducible diffusion processes using the pseudo-marginal approach
Stramer, O. and Bognar, M. (2011) · 2011
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Efficient implementation of Markov chain Monte Carlo when using an unbiased likelihood estimator
Doucet, A., Pitt, M., Deligiannidis, G., and Kohn, R. (2015) · 2015
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Sequential quasi Monte Carlo
Gerber, M. and Chopin, N. (2015) · 2015
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On the efficiency of the pseudo marginal random walk Metropolis algorithm
Sherlock, C., Thiery, A., Roberts, G., and Rosenthal, J. (2015) · 2015
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The use of a single pseudo-sample in approximate Bayesian computation
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The correlated pseudo-marginal method
Deligiannidis, G., Doucet, A., and Pitt, M. (2016) · 2016
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Gunawan, D., Tran, M.-N., Suzuki, K., Dick, J., and Kohn, R. (2016) · 2016
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Variational Bayes with intractable likelihood
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