---
title: "A Parallel Algorithm for Statistical Belief Refinement and its use in Causal Reasoning"
authors:
  - "Jay C. Weber"
doi: ""
url: "https://ijcai.org/proceedings/1989/144"
pdf: "https://ijcai.org/proceedings/1989/0144.pdf"
venue: "Proceedings of the Eleventh International Joint Conference on Artificial Intelligence"
year: 1989
pages: "900-904"
track: "Planning, Scheduling, Reasoning About Actions"
keywords:
---

# A Parallel Algorithm for Statistical Belief Refinement and its use in Causal Reasoning

Jay C. Weber

*Proceedings of the Eleventh International Joint Conference on Artificial Intelligence*, pages 900-904.

## Abstract

This paper presents a new approach to efficient parallel computation of statistical inferences. This approach involves two heuristics, highest impact first and highest impact remaining, which control the speed of convergence and error estimation for an algorithm that iteratively refines degrees ~f belief. When applied to causal reasoning, this algorithm provides a performance solution to the qualification problem. This algorithm has been implemented and tested by a program called HITEST, which runs on parallel hardware.