Bayesian linear models with unknown design over finite alphabets
wang, y.; Dutta, R.; Futschik, A.
Show abstract
Our topic is the reconstruction of the unknown matrices S and{omega} for the multivariate linear model Y = S{omega} +{varepsilon} under the assumption that the entries of S are drawn from the finite alphabet [A] = 0, 1 and{omega} is a weight matrix. While a frequentist method has recently been proposed for this purpose, a Bayesian approach seems also desirable. We therefore provide a new hierarchical Bayesian method for this inferential task. Our approach provides estimates of the posterior that may be used to quantify uncertainty. Since matching permutations in both S and{omega} lead to the same reconstruction S{omega}, we introduce an order-preserving shrinkage prior to establish identifiability with respect to permutations.
Matching journals
The top 9 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- MCMC-CE: A Novel and Efficient Algorithm for Estimating Small Right-Tail Probabilities of Quadratic Forms with Applications in Genomics 96%
- The statistics of k-mers from a sequence undergoing a simple mutation process without spurious matches 94%
- NetMix: A network-structured mixture model for reduced-bias estimation of altered subnetworks 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.