4 papers
Quantization of Probability Distributions via Divide-and-Conquer: Convergence and Error Propagation under Distributional Arithmetic Operations
Bilgesu Arif Bilgin, Olof Hallqvist Elias, Michael Selby +1
This article studies a general divide-and-conquer algorithm for approximating continuous one-dimensional probability distributions with finite mean. The article presents a numerica…
Distributional Computational Graphs: Error Bounds
Olof Hallqvist Elias, Michael Selby, Phillip Stanley-Marbell
We study a general framework of distributional computational graphs: computational graphs whose inputs are probability distributions rather than point values. We analyze the discre…
A Tensor Train Approach for Deterministic Arithmetic Operations on Discrete Representations of Probability Distributions
Gerhard Kirsten, Bilgesu Bilgin, Janith Petangoda +1
Computing with discrete representations of high-dimensional probability distributions is fundamental to uncertainty quantification, Bayesian inference, and stochastic modeling. How…
The Monte Carlo Method and New Device and Architectural Techniques for Accelerating It
Janith Petangoda, Chatura Samarakoon, James Meech +5
Computing systems interacting with real-world processes must safely and reliably process uncertain data. The Monte Carlo method is a popular approach for computing with such uncert…