Corpus ID: 235436266

A Horseshoe Pit mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging

@inproceedings{Denti2021AHP,
  title={A Horseshoe Pit mixture model for Bayesian screening with an application to light sheet fluorescence microscopy in brain imaging},
  author={Francesco Denti and Ricardo Azevedo and Chelsie Lo and Damian G. Wheeler and S. Gandhi and M. Guindani and B. Shahbaba},
  year={2021}
}
Finding parsimonious models through variable selection is a fundamental problem in many areas of statistical inference. Here, we focus on Bayesian regression models, where variable selection can be implemented through a regularizing prior imposed on the distribution of the regression coefficients. In the Bayesian literature, there are two main types of priors used to accomplish this goal: the spike-and-slab and the continuous scale mixtures of Gaussians. The former is a discrete mixture of two… Expand

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