Prior-preconditioned conjugate gradient for accelerated Gibbs sampling in "large n & large p" sparse Bayesian logistic regression models

In a modern observational study based on healthcare databases, the number of observations typically ranges in the order of 10^5 ~ 10^6 and that of the predictors in the order of 10^4 ~ 10^5. Despite the large sample size, data rarely provide sufficient information to reliably estimate such a large number of parameters. Sparse regression provides a potential solution. There is a rich literature on desirable theoretical properties of Bayesian approaches based on shrinkage priors. On the other hand, the development of scalable methods for the required posterior computation has largely been limited to the p >> n case. Shrinkage priors make the posterior amenable to Gibbs sampling, but a major computational bottleneck arises from the need to sample from a high-dimensional Gaussian distribution at each iteration. Despite a closed-form expression for the precision matrix $Φ$, computing and factorizing such a large matrix is computationally expensive nonetheless. In this article, we present a novel algorithm to speed up this bottleneck based on the following observation: we can cheaply generate a random vector $b$ such that the solution to the linear system $Φβ= b$ has the desired Gaussian distribution. We can then solve the linear system by the conjugate gradient (CG) algorithm through the matrix-vector multiplications by $Φ$, without ever explicitly inverting $Φ$. Practical performance of CG, however, depends critically on appropriate preconditioning of the linear system; we turn CG into an effective algorithm for sparse Bayesian regression by developing a theory of prior-preconditioning. We apply our algorithm to a large-scale observational study with n = 72,489 and p = 22,175, designed to assess the relative risk of intracranial hemorrhage from two alternative blood anti-coagulants. Our algorithm demonstrates an order of magnitude speed-up in the posterior computation.