University of Cambridge > Talks.cam > Statistics > Handling Sparsity via the Horseshoe

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Handling Sparsity via the Horseshoe

Download to your calendar using vCal

If you have a question about this talk, please contact .

This paper presents a general, fully Bayesian framework for sparse supervised-learning problems based on the horseshoe prior. The horseshoe prior is a member of the family of multivariate scale mixtures of normals, and is therefore closely related to widely used approaches for sparse Bayesian learning, including, among others, Laplacian (LASSO) and Student-t priors (relevance vector machines). The advantages of the horseshoe are its robustness at handling unknown sparsity and large outlying signals. These properties are justified theoretically via a representation theorem and accompanied by comprehensive empirical experiments that compare its performance to benchmark alternatives.

This talk is part of the Statistics series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

my list Goldsmiths 1 & https://us06web.zoom.us/j/81688365420?pwd=b3RpdHgxNVhWbWQrbXluMUxYNWVtQT09 Meeting ID: 816 8836 5420 Passcode: 047859 In person, SG1, Alison Richard Building, University of Cambridge

Other talks

TBC Cooperation in Nature Title to be confirmed Morning Tea and Coffee Morning Break / Group Photo Tea and Coffee Break
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity