Componentwise approximate Bayesian computation via Gibbs-like steps
通过类 Gibbs 步骤实现逐分量近似贝叶斯计算
Summary Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are, however, sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty we explore a Gibbs version of the approximate Bayesian computation approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior…