论文标题
关于使用离散的模拟数据来估计贝叶斯分析中隐性似然的注释
A Note on Using Discretized Simulated Data to Estimate Implicit Likelihoods in Bayesian Analyses
论文作者
论文摘要
本文提出了一种贝叶斯推论方法，其中模型的可能性未知，但是可以轻松地从模型中模拟数据。我们将模拟（连续）数据离散，以估算使用Markov链蒙特卡洛算法的贝叶斯分析中的隐式可能性。提供了三个示例，以及有关该方法的某些特性的一项小型研究。
This article presents a Bayesian inferential method where the likelihood for a model is unknown but where data can easily be simulated from the model. We discretize simulated (continuous) data to estimate the implicit likelihood in a Bayesian analysis employing a Markov chain Monte Carlo algorithm. Three examples are presented as well as a small study on some of the method's properties.
