鲁棒贝叶斯混合分布的模型选择

提出一种基于偏差信息准则(deriance information criterion,DIC)的鲁棒贝叶斯混合分布模型选择算法.在变分逼近框架下,给出鲁棒贝叶斯混合模型的DIC计算公式;设计的模型选择算法能同时学习模型参数推断和进行模型选择,避免在大的候选模型集中根据模型选择准则选取最优模型.给出试验参数初始值设置方法,在含有较多离群点的仿真数据和Old Faithful Geyser数据上的试验结果表明了好的性能:得到鲁棒的混合分量参数和较准确的混合分量个数.

Model selection for robust Bayesian mixture distributions

Bayesian approaches to robust mixture modelling based on Student-t distributions enable to be less sensitive to outliers,thereby preventing from over-estimating of the number of mixting components.However,there are two intractable problems in the previous methods for model selection under the variational Bayesian framework:(1) The variational approach converges to a local maximum of the low bound on the log-evidence that dependents on the initial parameter values.How can the variational approach guarantee t...