论文标题
贝叶斯计算具有棘手的可能性
Bayesian Computation with Intractable Likelihoods
论文作者
论文摘要
本文调查了具有顽固性可能性的后验推断的计算方法，这是可能以封闭形式的可能性函数或对可能性评估的地方。我们审查了伪划分方法，近似贝叶斯计算（ABC），交换算法，热力学整合和复合可能性的最新发展，并特别注意大型数据集的可扩展性方面的进步。我们还提到了用于实现这些算法的R和MATLAB源代码。
This article surveys computational methods for posterior inference with intractable likelihoods, that is where the likelihood function is unavailable in closed form, or where evaluation of the likelihood is infeasible. We review recent developments in pseudo-marginal methods, approximate Bayesian computation (ABC), the exchange algorithm, thermodynamic integration, and composite likelihood, paying particular attention to advancements in scalability for large datasets. We also mention R and MATLAB source code for implementations of these algorithms, where they are available.
