|
Activity Number:
|
355
- Advanced Bayesian Topics (Part 4)
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Thursday, August 12, 2021 : 10:00 AM to 11:50 AM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
|
Abstract #318810
|
|
|
Title:
|
Continuous shrinkage priors with dependence
|
|
Author(s):
|
Toryn Schafer* and David S Matteson
|
|
Companies:
|
Cornell University and Cornell University
|
|
Keywords:
|
bayesian learning;
spatial data;
shrinkage prior;
time series;
dependence;
generalized linear model
|
|
Abstract:
|
Continuous shrinkage priors have gained popularity in Bayesian models for learning sparse signals. Inducing dependence in the latent sparsity variables of a shrinkage prior allows for learning of locally smooth trends in space or time with potentially abrupt changes. For example, in time series modeling dependence in the dynamic shrinkage prior results in neighboring time points are likely to have similar sparsity. We present extensions of the dynamic shrinkage prior for broader applicability.
|
Authors who are presenting talks have a * after their name.
