论文标题
强大而有效的近似贝叶斯计算：最小距离方法
Robust and Efficient Approximate Bayesian Computation: A Minimum Distance Approach
论文作者
论文摘要
在许多情况下，近似贝叶斯方法的应用受到了两个实际特征的阻碍：1）将数据投射到低维摘要的要求，包括选择该投影，最终会导致推断效率低下； 2）可能缺乏与基础模型结构偏差的稳健性。由于这些效率和鲁棒性的关注，我们构建了一种新的贝叶斯方法，该方法可以在基础模型得到充分指定时可以提供有效的估计器，并且对某些形式的模型错误指定同时强大。这种新方法通过考虑经验和模拟概率度量之间的规范来绕过摘要的计算。对于规范的特定选择，我们证明了这种方法可以提供与使用精确贝叶斯推理获得的点估计器一样有效的，同时也表现出与基础模型假设偏差的稳健性。
In many instances, the application of approximate Bayesian methods is hampered by two practical features: 1) the requirement to project the data down to low-dimensional summary, including the choice of this projection, which ultimately yields inefficient inference; 2) a possible lack of robustness to deviations from the underlying model structure. Motivated by these efficiency and robustness concerns, we construct a new Bayesian method that can deliver efficient estimators when the underlying model is well-specified, and which is simultaneously robust to certain forms of model misspecification. This new approach bypasses the calculation of summaries by considering a norm between empirical and simulated probability measures. For specific choices of the norm, we demonstrate that this approach can deliver point estimators that are as efficient as those obtained using exact Bayesian inference, while also simultaneously displaying robustness to deviations from the underlying model assumptions.
