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Graphical prior elicitation in univariate models
- Source :
- Communications in Statistics - Simulation and Computation. 47:2906-2924
- Publication Year :
- 2017
- Publisher :
- Informa UK Limited, 2017.
-
Abstract
- Standard prior elicitation procedures require experts to explicitly quantify their beliefs about parameters in the form of multiple summaries. In this article, we draw on recent advances in the statistical graphics and information visualization communities to propose a novel elicitation scheme that implicitly learns an expert’s opinions through their sequential selection of graphics of carefully constructed hypothetical future samples. While the scheme can be applied to a broad array of models, we use it to construct procedures for elicitation in data models commonly used in practice: Bernoulli, Poisson, and Normal. We also provide open-source, web-based Shiny implementations of the procedures.
- Subjects :
- Statistics and Probability
Scheme (programming language)
business.industry
Univariate
020207 software engineering
02 engineering and technology
Construct (python library)
Machine learning
computer.software_genre
01 natural sciences
Data modeling
Bayesian statistics
010104 statistics & probability
Information visualization
Modeling and Simulation
Statistics
0202 electrical engineering, electronic engineering, information engineering
Artificial intelligence
0101 mathematics
Graphics
business
computer
Statistical graphics
Mathematics
computer.programming_language
Subjects
Details
- ISSN :
- 15324141 and 03610918
- Volume :
- 47
- Database :
- OpenAIRE
- Journal :
- Communications in Statistics - Simulation and Computation
- Accession number :
- edsair.doi...........3e2cd09506c3d3f0fca84b58bc8f6e7e