Search Results for author:
Found 6 papers, 4 papers with code
Deep importance sampling using tensor trains with application to a priori and a posteriori rare event estimation
We approximate the optimal importance distribution in a general importance sampling problem as the pushforward of a reference distribution under a composition of order-preserving transformations, in which each transformation is formed by a squared tensor-train decomposition.
Multilevel Delayed Acceptance MCMC with an Adaptive Error Model in PyMC3
Uncertainty Quantification through Markov Chain Monte Carlo (MCMC) can be prohibitively expensive for target probability densities with expensive likelihood functions, for instance when the evaluation it involves solving a Partial Differential Equation (PDE), as is the case in a wide range of engineering applications.
Probabilistic Programming
Computation
Multilevel Monte Carlo for quantum mechanics on a lattice
This paper discusses hierarchical sampling methods to tame this growth in autocorrelations.
High Energy Physics - Lattice Numerical Analysis Numerical Analysis Computational Physics 81-08, 81T25, 65Y20, 60J22 F.2; J.2
HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference
Many recent invertible neural architectures are based on coupling block designs where variables are divided in two subsets which serve as inputs of an easily invertible (usually affine) triangular transformation.
Approximation and sampling of multivariate probability distributions in the tensor train decomposition
We find that the importance-weight corrected quasi-Monte Carlo quadrature performs best in all computed examples, and is orders-of-magnitude more efficient than DRAM across a wide range of approximation accuracies and sample sizes.
Numerical Analysis Probability Statistics Theory Statistics Theory 65D15, 65D32, 65C05, 65C40, 65C60, 62F15, 15A69, 15A23
A Stein variational Newton method
Stein variational gradient descent (SVGD) was recently proposed as a general purpose nonparametric variational inference algorithm [Liu & Wang, NIPS 2016]: it minimizes the Kullback-Leibler divergence between the target distribution and its approximation by implementing a form of functional gradient descent on a reproducing kernel Hilbert space.
