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Bayesian synthetic likelihood (BSL) is a popular method for performing approximate Bayesian inference when the likelihood function is intractable.
Bayesian inference using synthetic likelihood: asymptotics and adjustments
Frazier, D. T., Nott, D. J., Drovandi, C., and Kohn, R. (2019) · 1902
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Robust approximate Bayesian inference with synthetic likelihood
Frazier, D. T. and Drovandi, C. (2020) · 1904
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Efficient Bayesian synthetic likelihood with whitening transformations
Priddle, J. W., Sisson, S. A., Frazier, D. T., and Drovandi, C. (2020) · 1909
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Combined parameter and state inference with automatically calibrated ABC
Ebert, A., Pudlo, P., Mengersen, K., and Wu, P. (2019) · 1910
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A nonparametric estimate of a multivariate density function
Loftsgaarden, D. O., Quesenberry, C. P., et al. (1965) · 1965
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A simplex method for function minimization
Nelder, J. A. and Mead, R. (1965) · 1965
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Variable kernel estimates of multivariate densities
Breiman, L., Meisel, W., and Purcell, E. (1977) · 1977
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Density estimation for statistics and data analysis
Silverman, B. W. (1986) · 1986
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Transformations in density estimation
Wand, M. P., Marron, J. S., and Ruppert, D. (1991) · 1991
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Stochastic volatility: likelihood inference and comparison with ARCH models
Kim, S., Shephard, N., and Chib, S. (1998) · 1998
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Improved fast Gauss transform and efficient kernel density estimation
Yang, C., Duraiswami, R., Gumerov, N. A., and Davis, L. (2003) · 2003
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Bayesian inference for generalised Markov switching stochastic volatility models
Casarin, R. (2004) · 2004
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Adaptive MCMC for synthetic likelihoods and correlated synthetic likelihoods
Picchini, U., Simola, U., and Corander, J. (2020) · 2004
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Kernel density estimation for heavy-tailed distributions using the Champernowne transformation
Buch-Larsen, T., Nielsen, J. P., Guillén, M., and Bolancé, C. (2005) · 2005
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Copula modeling: an introduction for practitioners
Trivedi, P. K., Zimmer, D. M., et al. (2007) · 2007
Cited alongside, same era.
Penalized normal likelihood and ridge regularization of correlation and covariance matrices
Warton, D. I. (2008) · 2008
Cited alongside, same era.
Sinh-arcsinh distributions
Jones, M. C. and Pewsey, A. (2009) · 2009
Cited alongside, same era.
Non-linear regression models for approximate Bayesian computation
Blum, M. G. and François, O. (2010) · 2010
Cited alongside, same era.
Statistical inference for noisy nonlinear ecological dynamic systems
Wood, S. N. (2010) · 2010
Cited alongside, same era.
The Gaussian rank correlation estimator: robustness properties
Boudt, K., Cornelissen, J., and Croux, C. (2012) · 2012
Cited alongside, same era.
On hyperbolic transformations to normality
Tsai, A. C., Liou, M., Simak, M., and Cheng, P. E. (2017) · 2017
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An extended empirical saddlepoint approximation for intractable likelihoods
Fasiolo, M., Wood, S. N., Hartig, F., Bravington, M. V., et al. (2018) · 2018
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Optimal whitening and decorrelation
Kessy, A., Lewin, A., and Strimmer, K. (2018) · 2018
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Approximate Bayesian computation and simulation-based inference for complex stochastic epidemic models
McKinley, T. J., Vernon, I., Andrianakis, I., McCreesh, N., Oakley, J. E., Nsubuga, R. N., Goldstein, M., White, R. G., et al. (2018) · 2018
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Sequential neural likelihood: Fast likelihood-free inference with autoregressive flows
Papamakarios, G., Sterratt, D. C., and Murray, I. (2018) · 2018
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Approximate Bayesian computational methods
Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012) · 2012
Cited alongside, same era.
Approximate Bayesian computation by subset simulation
Chiachio, M., Beck, J. L., Chiachio, J., and Rus, G. (2014) · 2014
Cited alongside, same era.
On Bayesian inference for the M/G/1 queue with efficient MCMC sampling
Shestopaloff, A. Y. and Neal, R. M. (2014) · 2014
Cited alongside, same era.
Variational inference with normalizing flows
Rezende, D. J. and Mohamed, S. (2015) · 2015
Cited alongside, same era.
Improved variational inference with inverse autoregressive flow
Kingma, D. P., Salimans, T., Jozefowicz, R., Chen, X., Sutskever, I., and Welling, M. (2016) · 2016
Cited alongside, same era.
Bootstrapped synthetic likelihood
Everitt, R. G. (2017) · 2017
Cited alongside, same era.
Transport map accelerated Markov chain Monte Carlo
Parno, M. D. and Marzouk, Y. M. (2018) · 2018
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Bayesian synthetic likelihood
Price, L. F., Drovandi, C. C., Lee, A., and Nott, D. J. (2018) · 2018
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Accelerating Bayesian synthetic likelihood with the graphical lasso
An, Z., South, L. F., Nott, D. J., and Drovandi, C. C. (2019) · 2019
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Calibrating an individual-based movement model to predict functional connectivity for little owls
Hauenstein, S., Fattebert, J., Grüebler, M. U., Naef-Daenzer, B., Pe’er, G., and Hartig, F. (2019) · 2019
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Resolving outbreak dynamics using approximate Bayesian computation for stochastic birth–death models
Lintusaari, J., Blomstedt, P., Rose, B., Sivula, T., Gutmann, M. U., Kaski, S., and Corander, J. (2019) · 2019
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Filtering and estimation for a class of stochastic volatility models with intractable likelihoods
Vankov, E. R., Guindani, M., Ensor, K. B., et al. (2019) · 2019
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Robust Bayesian synthetic likelihood via a semi-parametric approach
An, Z., Nott, D. J., and Drovandi, C. (2020) · 2020
Closest in time.
Estimating a novel stochastic model for within-field disease dynamics of banana bunchy top virus via approximate Bayesian computation
Varghese, A., Drovandi, C., Mira, A., and Mengersen, K. (2020) · 2020
Closest in time.