We show that lower-dimensional marginal densities of dependent zero-mean normal distributions truncated to the positive orthant exhibit a mass-shifting phenomenon. Despite the truncated multivariat…
Proximal Markov chain Monte Carlo is a novel construct that lies at the intersection of Bayesian computation and convex optimization, which helped popularize the use of nondifferentiable priors in …
In this work, we propose a novel approximated collapsed variational Bayes approach to model selection in linear regression. The approximated collapsed variational Bayes algorithm offers improvement…
In this paper, we propose second-order sufficient optimality conditions for a very general nonconvex constrained optimization problem, which covers many prominent mathematical programs. Unlike the …
On-demand delivery through sharing platforms represents a rapidly expanding segment of the global workforce. The emergence of sharing platforms enables gig workers to choose when and where to work,…
We provide a reduced-rank approach (RRA) to extract a few factors from a large set of factor proxies and apply the extracted factors to model the cross-section of expected stock returns. Empiricall…
Identification of treatment effects in the presence of unmeasured confounding is a persistent problem in the social, biological, and medical sciences. The problem of unmeasured confounding in setti…
We demonstrated the high energy 1.65 μm singular beam generation from a dissipative soliton resonance (DSR) all-fiber Raman laser experimentally. By utilizing advantages of the high nonlinearity a…
We propose a deep generative approach to sampling from a conditional distribution based on a unified formulation of conditional distribution and generalized nonparametric regression function using …
In this article, a novel Bayes-Adam-based multiple-input multiple-output (MIMO) equalizer was proposed and experimentally demonstrated for an orbital angular momentum (OAM) mode-division multiplexe…