Multiset Model Selection
提出多重集模型选择框架，通过多重集高效探索高维相关协变量的模型空间，无需大量调参即可获得后验模型概率，适用于线性和广义线性模型。
Model selection algorithms are required to efficiently traverse the space of models. In problems with high-dimensional and possibly correlated covariates, efficient exploration of the model space becomes a challenge. To overcome this, a multiset is placed on the model space to enable efficient exploration of multiple model modes with minimal tuning. The multiset model selection (MSMS) framework is based on independent priors for the parameters and model indicators on variables. Posterior model probabilities can be easily obtained from multiset averaged posterior model probabilities in MSMS. The effectiveness of MSMS is demonstrated for linear and generalized linear models. Supplementary material for this article is available online.