Robust Bayesian Inference for Simulator-based Models via the MMD Posterior Bootstrap

Bootstrapped synthetic likelihood

Approximate Bayesian computation (ABC) and synthetic likelihood (SL) techniques have enabled the use of Bayesian inference for models that may be simulated, but for which the likelihood cannot be evaluated pointwise at values of an unknown parameter $θ$. The main idea in ABC and SL is to, for different values of $θ$ (usually chosen using a Monte Carlo algorithm), build estimates of the likelihood based on simulations from the model conditional on $θ$. The quality of these estimates determines the efficiency of an ABC/SL algorithm. In standard ABC/SL, the only means to improve an estimated like…

Stratified sampling and bootstrapping for approximate Bayesian computation

Approximate Bayesian computation (ABC) is computationally intensive for complex model simulators. To exploit expensive simulations, data-resampling via bootstrapping can be employed to obtain many artificial datasets at little cost. However, when using this approach within ABC, the posterior variance is inflated, thus resulting in biased posterior inference. Here we use stratified Monte Carlo to considerably reduce the bias induced by data resampling. We also show empirically that it is possible to obtain reliable inference using a larger than usual ABC threshold. Finally, we show that with st…

利用蒙特卡洛对数似然估计器元模型的隐式定义模型可扩展仿真推断

Scalable simulation-based inference for implicitly defined models using a metamodel for Monte Carlo log-likelihood estimator

本文针对隐式定义模型提出一种基于蒙特卡洛对数似然估计器元模型的可扩展仿真推断方法，利用局部渐近正态性量化参数不确定性，并通过多个示例验证其准确性与可扩展性。