A Parallel Algorithm for Statistical Belief Refinement and its use in Causal Reasoning
Proceedings of the Eleventh International Joint Conference on Artificial Intelligence. Planning, Scheduling, Reasoning About Actions. 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.
@inproceedings{ijcai1989p144,
title = {A Parallel Algorithm for Statistical Belief Refinement and its use in Causal Reasoning},
author = {Weber, Jay C.},
booktitle = {Proceedings of the Eleventh International Joint Conference on
Artificial Intelligence, {IJCAI-89}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {N. S. Sridharan},
pages = {900--904},
year = {1989},
month = {8},
note = {Planning, Scheduling, Reasoning About Actions},
url = {https://ijcai.org/proceedings/1989/144},
}
TY - CPAPER TI - A Parallel Algorithm for Statistical Belief Refinement and its use in Causal Reasoning AU - Weber, Jay C. T2 - Proceedings of the Eleventh International Joint Conference on Artificial Intelligence, IJCAI-89 PB - International Joint Conferences on Artificial Intelligence PY - 1989 SP - 900 EP - 904 UR - https://ijcai.org/proceedings/1989/144 ER -Copied
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