VBMS: Variational Bayesian Algorithm for Multi-Source Heterogeneous
Models
A Variational Bayesian algorithm for high-dimensional multi-source
heterogeneous linear models. More details have been written up in a paper
submitted to the journal Statistics in Medicine, and the details of variational
Bayesian methods can be found in Ray and Szabo (2021) <doi:10.1080/01621459.2020.1847121>.
It simultaneously performs parameter estimation and variable selection. The
algorithm supports two model settings: (1) local models, where variable selection
is only applied to homogeneous coefficients, and (2) global models, where variable
selection is also performed on heterogeneous coefficients. Two forms of
Spike-and-Slab priors are available: the Laplace distribution and the Gaussian
distribution as the Slab component.
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