no code implementations • 22 Jun 2022 • Ricardo Baptista, Lianghao Cao, Joshua Chen, Omar Ghattas, Fengyi Li, Youssef M. Marzouk, J. Tinsley Oden
We tackle this challenging Bayesian inference problem using a likelihood-free approach based on measure transport together with the construction of summary statistics for the image data.
1 code implementation • 19 Apr 2022 • Alex Leviyev, Joshua Chen, Yifei Wang, Omar Ghattas, Aaron Zimmerman
Meanwhile, Stein variational Newton (SVN), a Newton-like extension of SVGD, dramatically accelerates the convergence of SVGD by incorporating Hessian information into the dynamics, but also produces biased samples.
1 code implementation • NeurIPS 2019 • Peng Chen, Keyi Wu, Joshua Chen, Tom O'Leary-Roseberry, Omar Ghattas
We propose a projected Stein variational Newton (pSVN) method for high-dimensional Bayesian inference.