Multiset Model Selection

作者：Hoegh Andrew^*; Maiti Dipayan; Leman Scotland

来源：Journal of Computational and Graphical Statistics, 2018, 27(2): 436-448.

DOI：10.1080/10618600.2017.1379408

• Bayesian modeling
• MCMC
• Variable selection

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.

• 出版日期2018
• 单位美国弗吉尼亚理工大学(Virginia Tech)