Revision of possibility distributions: A Bayesian inference pattern

AbstractThe main lines ofthe program designed for the interpretation of ECGs, developed in Padova by LADSEB-CNR with the cooperation of the Medical School of the University of Padova are described. In particular, the strategies used for (i) morphology recognition, (ii) measurement evaluation, and (iii) linguistic decision making are illustrated. The main aspect which discerns this program in comparison with other approaches to computerized electrocardiography is its ability of managing the imprecision in both the measurements and the medical knowledge through the use of fuzzy-set methodologies. So-called possibility distributions are used to represent ill-defined parameters as well as threshold limits for diagnostic criteria. In this way, smooth conclusions are derived when the evidence does not support a crisp decision. The influence of the CSE project on the evolution of the Padova program is illustrated.

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Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

10.3115/v1/p15-1081 ◽

2015 ◽

Cited By ~ 1

Author(s):

Qing Dou ◽

Ashish Vaswani ◽

Kevin Knight ◽

Chris Dyer

Keyword(s):

Bayesian Inference ◽

Vector Space ◽

Vector Space Models

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How do academics assess the results of multiple experiments?

10.31234/osf.io/xyks4 ◽

2018 ◽

Cited By ~ 1

Author(s):

Olmo Van den Akker ◽

Linda Dominguez Alvarez ◽

Marjan Bakker ◽

Jelte M. Wicherts ◽

Marcel A. L. M. van Assen

Keyword(s):

Bayesian Inference ◽

Secondary Analysis ◽

Individual Participant Data ◽

Additional Experiment ◽

Underlying Theory ◽

Individual Participant ◽

Vote Counting

We studied how academics assess the results of a set of four experiments that all test a given theory. We found that participants’ belief in the theory increases with the number of significant results, and that direct replications were considered to be more important than conceptual replications. We found no difference between authors and reviewers in their propensity to submit or recommend to publish sets of results, but we did find that authors are generally more likely to desire an additional experiment. In a preregistered secondary analysis of individual participant data, we examined the heuristics academics use to assess the results of four experiments. Only 6 out of 312 (1.9%) participants we analyzed used the normative method of Bayesian inference, whereas the majority of participants used vote counting approaches that tend to undervalue the evidence for the underlying theory if two or more results are statistically significant.

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