全部文献期刊学位论文会议报纸专利标准年鉴图书|学者科研项目
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作者:David M. Blei , Kenneth A. Norman
来源:[J].NeuroImage(IF 6.252), 2014, Vol.98, pp.91-102Elsevier
摘要:Abstract(#br)This paper extends earlier work on spatial modeling of fMRI data to the temporal domain, providing a framework for analyzing high temporal resolution brain imaging modalities such as electroencapholography (EEG). The central idea is to decompose brain imaging data in...
作者:Rajesh Ranganath , David M. Blei
来源:[J].Journal of the American Statistical Association(IF 1.834), 2018, Vol.113 (521), pp.417-430Taylor & Francis
摘要:ABSTRACT(#br)We develop correlated random measures, random measures where the atom weights can exhibit a flexible pattern of dependence, and use them to develop powerful hierarchical Bayesian nonparametric models. Hierarchical Bayesian nonparametric models are usually built from ...
作者:Yixin Wang , David M. Blei
来源:[J].Journal of the American Statistical Association(IF 1.834), 2019, Vol.114 (527), pp.1147-1161Taylor & Francis
摘要:ABSTRACT(#br)A key challenge for modern Bayesian statistics is how to perform scalable inference of posterior distributions. To address this challenge, variational Bayes (VB) methods have emerged as a popular alternative to the classical Markov chain Monte Carlo (MCMC) metho...
作者:Samuel J. Gershman , David M. Blei
来源:[J].Journal of Mathematical Psychology(IF 1.622), 2012, Vol.56 (1), pp.1-12Elsevier
摘要:Abstract(#br)A key problem in statistical modeling is model selection, that is, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in fa...
作者:David M. Blei , Alp Kucukelbir , Jon D. McAuliffe
来源:[J].Journal of the American Statistical Association(IF 1.834), 2017, Vol.112 (518), pp.859-877Taylor & Francis
摘要:ABSTRACT(#br)One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the posterior ...

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