Bayesian synthetic likelihood (BSL, Price et al. (2018) ) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). uBSL (Price et al. (2018) ) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) ) is more robust to non-normal summary statistics. BSLmisspec (Frazier et al. 2...
Approximate Bayesian computation (ABC) has become an essential tool for the anal-ysis of complex sto...
Synthetic likelihood (SL) is a strategy for parameter inference when the likelihood function is anal...
One popular approach to likelihood-free inference is the synthetic likelihood method, which assumes ...
Bayesian synthetic likelihood (BSL; Price, Drovandi, Lee, and Nott 2018) is a popular method for est...
Bayesian synthetic likelihood (BSL) is now a well-established method for performing approximate Baye...
Having the ability to work with complex models can be highly beneficial, but the computational cost ...
Bayesian synthetic likelihood (BSL) is now an established method for conducting approximate Bayesian...
Likelihood-free methods are an established approach for performing approximate Bayesian inference fo...
Implementing Bayesian inference is often computationally challenging in complex models, especially w...
Implementing Bayesian inference is often computationally challenging in complex models, especially w...
Simulation-based Bayesian inference methods are useful when the statistical model of interest does n...
Simulation-based Bayesian inference methods are useful when the statistical model of interest does n...
The conceptual and methodological framework that underpins approximate Bayesian computation (ABC) is...
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Ga...
Summary. Approximate Bayesian Computations (ABC) are considered to be noisy. We show that ABC can be...
Approximate Bayesian computation (ABC) has become an essential tool for the anal-ysis of complex sto...
Synthetic likelihood (SL) is a strategy for parameter inference when the likelihood function is anal...
One popular approach to likelihood-free inference is the synthetic likelihood method, which assumes ...
Bayesian synthetic likelihood (BSL; Price, Drovandi, Lee, and Nott 2018) is a popular method for est...
Bayesian synthetic likelihood (BSL) is now a well-established method for performing approximate Baye...
Having the ability to work with complex models can be highly beneficial, but the computational cost ...
Bayesian synthetic likelihood (BSL) is now an established method for conducting approximate Bayesian...
Likelihood-free methods are an established approach for performing approximate Bayesian inference fo...
Implementing Bayesian inference is often computationally challenging in complex models, especially w...
Implementing Bayesian inference is often computationally challenging in complex models, especially w...
Simulation-based Bayesian inference methods are useful when the statistical model of interest does n...
Simulation-based Bayesian inference methods are useful when the statistical model of interest does n...
The conceptual and methodological framework that underpins approximate Bayesian computation (ABC) is...
Synthetic likelihood is an attractive approach to likelihood-free inference when an approximately Ga...
Summary. Approximate Bayesian Computations (ABC) are considered to be noisy. We show that ABC can be...
Approximate Bayesian computation (ABC) has become an essential tool for the anal-ysis of complex sto...
Synthetic likelihood (SL) is a strategy for parameter inference when the likelihood function is anal...
One popular approach to likelihood-free inference is the synthetic likelihood method, which assumes ...
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