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A qualitative study of probability density visualization techniques in measurements
- Publication Year :
- 2015
- Publisher :
- Elsevier, 2015.
-
Abstract
- Engineers find interpreting plots of a measured physical variable more straightforward than doing a formal statistical analysis. The default choice to display the data behavior is the histogram. The histogram’s performance has proved to be sufficient. However, histograms have a number of limitations including sensitivity to the binwidth and a non-physical roughness. Over the past years, statisticians have developed different techniques to address these problems. These techniques provide a much clearer visualization of the probability density and a more accurate estimation of the statistical properties of the measured data. Despite their increasing use in other fields, these techniques are rarely used in the measurement community. For instance, most measurement instruments provide histograms only. This review article revisits these techniques from an engineer viewpoint to encourage its use. Different examples that include known and unknown densities result in practical guidelines that help the measurement engineer to visualize the probability content.
- Subjects :
- Density estimation
Computer science
media_common.quotation_subject
Kernel density estimation
Probability density function
engineer
Machine learning
computer.software_genre
Polynomial function
Orthogonal series estimation
Histogram
probability density function
Nonparametric
Electrical and Electronic Engineering
Uncertainty characterization
Instrumentation
media_common
Creative visualization
business.industry
Applied Mathematics
Nonparametric statistics
Measurement instrument
Condensed Matter Physics
Multivariate kernel density estimation
Visualization
Artificial intelligence
Data mining
kernel density estimation
business
computer
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....a6d5fc7443bac9a888fade13b2ed9e2c