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Sound, Complete, Linear-Space, Best-First Diagnosis Search
Various model-based diagnosis scenarios require the computation of the m...
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Memory-Limited Model-Based Diagnosis
Various model-based diagnosis scenarios require the computation of most ...
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Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities
The multiple extension problem arises frequently in diagnostic and defau...
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Bayesian Sample Size Determination of Vibration Signals in Machine Learning Approach to Fault Diagnosis of Roller Bearings
Sample size determination for a data set is an important statistical pro...
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DynamicHS: Streamlining Reiter's Hitting-Set Tree for Sequential Diagnosis
Given a system that does not work as expected, Sequential Diagnosis (SD)...
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Joins on Samples: A Theoretical Guide for Practitioners
Despite decades of research on approximate query processing (AQP), our u...
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Sampling with censored data: a practical guide
In this review, we present a simple guide for researchers to obtain pseu...
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Do We Really Sample Right In Model-Based Diagnosis?
Statistical samples, in order to be representative, have to be drawn from a population in a random and unbiased way. Nevertheless, it is common practice in the field of model-based diagnosis to make estimations from (biased) best-first samples. One example is the computation of a few most probable possible fault explanations for a defective system and the use of these to assess which aspect of the system, if measured, would bring the highest information gain. In this work, we scrutinize whether these statistically not well-founded conventions, that both diagnosis researchers and practitioners have adhered to for decades, are indeed reasonable. To this end, we empirically analyze various sampling methods that generate fault explanations. We study the representativeness of the produced samples in terms of their estimations about fault explanations and how well they guide diagnostic decisions, and we investigate the impact of sample size, the optimal trade-off between sampling efficiency and effectivity, and how approximate sampling techniques compare to exact ones.
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