Bayesian sparsity

January 8, 2019 — November 19, 2024

Bayes
high d
information
model selection
regression
sparser than thou
statistics

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What if I like the flavours of both Bayesian inference and the implicit model selection of sparse inference? Can I cook Bayesian-Frequentist fusion cuisine with this novelty ingredient? It turns out that yes, we can add a variety of imitation sparsity flavours to Bayes model selection. The resulting methods are handy in, for example, symbolic system identification.

Figure 1

1 Laplace Prior

Laplace priors on linear regression coefficients include normal lasso as a MAP estimate.

Pro: It is easy to derive frequentist LASSO as a MAP estimate from this prior.

Con: Full posterior is not sparse, only the MAP estimate.

I have no need for this right now, but if I did, I might start with Dan Simpson’s critique.

2 Spike-and-slab prior

Probabilistic equivalent of a LASSO-type prior, via a hierarchical mixture, i.e. an inclusion probability for each coefficient.

Pro: Full posterior can be sparse in some sense.

Con: Mixtures of discrete and continuous variables like this are fiddly to deal with in MCMC, and just in general.

🏗

3 Horseshoe prior

Figure 2

Stan guy, Michael Betancourt introduces some issues with LASSO-type inference for Bayesians with a slant towards Horseshoe-type priors in preference to spike-and-slab, possibly because hierarchical mixtures like spike-and-slab are not that easy in Stan, albeit possible.

4 Thompson sampling for large model spaces

This looks cool: Liu and Ročková ().

5 Transdimensional inference

See transdimensional.

6 Global-local shrinkage hierarchy

Not quite sure what this is, but here are some papers: Bhadra et al. ();Polson and Scott ();Schmidt and Makalic ();Xu et al. ().

7 As Singular Learning

Sparse bayes problems are clearly connecting to singular learning theory approaches to Bayes. TBD

8 Incoming

9 References

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Bhadra, Datta, Polson, et al. 2016. “Default Bayesian Analysis with Global-Local Shrinkage Priors.” Biometrika.
Bhadra, Datta, Polson, et al. 2019. “Lasso Meets Horseshoe : A Survey.”
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———. 2010. “The Horseshoe Estimator for Sparse Signals.” Biometrika.
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Titsias, and Lázaro-Gredilla. 2011. “Spike and Slab Variational Inference for Multi-Task and Multiple Kernel Learning.” In Advances in Neural Information Processing Systems 24.
Wang, Sarkar, Carbonetto, et al. 2020. “A Simple New Approach to Variable Selection in Regression, with Application to Genetic Fine Mapping.” Journal of the Royal Statistical Society Series B: Statistical Methodology.
Xu, Schmidt, Makalic, et al. 2017. “Bayesian Sparse Global-Local Shrinkage Regression for Selection of Grouped Variables.”
Zhou, Chen, Paisley, et al. 2009. “Non-Parametric Bayesian Dictionary Learning for Sparse Image Representations.” In Proceedings of the 22nd International Conference on Neural Information Processing Systems. NIPS’09.