# Bayesian inference using synthetic likelihood: asymptotics and adjustments

@article{Frazier2019BayesianIU, title={Bayesian inference using synthetic likelihood: asymptotics and adjustments}, author={David T. Frazier and David J. Nott and Christopher C. Drovandi and Robert Kohn}, journal={arXiv: Computation}, year={2019} }

Implementing Bayesian inference is often computationally challenging in applications involving complex models, and sometimes calculating the likelihood itself is difficult. Synthetic likelihood is one approach for carrying out inference when the likelihood is intractable, but it is straightforward to simulate from the model. The method constructs an approximate likelihood by taking a vector summary statistic as being multivariate normal, with the unknown mean and covariance matrix estimated by… CONTINUE READING

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## BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood.

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CITES BACKGROUND

## Efficient Bayesian synthetic likelihood with whitening transformations.

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CITES METHODS & BACKGROUND

## Adaptive MCMC for synthetic likelihoods and correlated synthetic likelihoods.

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CITES METHODS

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