Bayesian inference for spatio-temporal spike-and-slab priors
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Journal of Machine Learning Research, Volume 18
AbstractIn this work, we address the problem of solving a series of underdetermined linear inverse problemblems subject to a sparsity constraint. We generalize the spike-and-slab prior distribution to encode a priori correlation of the support of the solution in both space and time by imposing a transformed Gaussian process on the spike-and-slab probabilities. An expectation propagation (EP) algorithm for posterior inference under the proposed model is derived. For large scale problems, the standard EP algorithm can be prohibitively slow. We therefore introduce three different approximation schemes to reduce the computational complexity. Finally, we demonstrate the proposed model using numerical experiments based on both synthetic and real data sets.
Bayesian inference, Expectation propagation, Linear inverse problems, Sparsity-promoting priors, Spike-and-slab priors
Andersen , M R , Vehtari , A , Winther , O & Kai Hansen , L 2017 , ' Bayesian inference for spatio-temporal spike-and-slab priors ' , Journal of Machine Learning Research , vol. 18 , pp. 1-58 . < http://www.jmlr.org/papers/v18/15-464.html >