https://github.com/bamresearch/probeye
A general framework for setting up parameter estimation problems.
Science Score: 49.0%
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Low similarity (12.9%) to scientific vocabulary
Repository
A general framework for setting up parameter estimation problems.
Basic Info
Statistics
- Stars: 5
- Watchers: 3
- Forks: 6
- Open Issues: 14
- Releases: 1
Metadata Files
README.md
probeye
This package provides a transparent and easy-to-use framework for solving parameter estimation problems (i.e., inverse problems) primarily via sampling methods in a characteristic two-step approach.
- In the first step, the problem at hand is defined in a solver-independent fashion, i.e., without specifying which computational means are supposed to be utilized for finding a solution.
- In the second step, the problem definition is handed over to a user-selected solver, that finds a solution to the problem. The currently supported solvers focus on Bayesian methods for posterior sampling.
Due to the broad variety of existing inference engine packages, probeye does not contain self-written implementations of solvers but merely interfaces with existing ones. It currently provides interfaces with emcee for MCMC sampling and with dynesty for nested sampling. It also provides two point-estimate solvers for maximum likelihood as well as maximum a-posteriori estimates based on scipy.
The parameter estimation problems probeye aims at are problems that are centered around forward models that are computationally expensive (e.g., parameterized finite element models), and the corresponding observations of which are not particularly numerous (typically around tens or hundreds of experiments). Such problems are often encountered in engineering problems where simulation models are calibrated based on laboratory tests, which are - due to their relatively high costs - not available in high numbers.
The source code of probeye is jointly developed by Bundesanstalt für Materialforschung und -prüfung (BAM) and Netherlands Organisation for applied scientific research (TNO) for calibrating parameterized physics-based models and quantifying uncertainties in the obtained parameter estimates.
Documentation
A documentation including explanations on the package's use as well as some examples can be found here.
Owner
- Name: Bundesanstalt für Materialforschung und -prüfung
- Login: BAMresearch
- Kind: organization
- Email: oss@bam.de
- Location: Berlin/Germany
- Website: www.bam.de
- Repositories: 36
- Profile: https://github.com/BAMresearch
German Federal scientific research institute for materials testing and research
GitHub Events
Total
- Issues event: 4
- Delete event: 5
- Issue comment event: 4
- Push event: 18
- Pull request event: 7
- Pull request review comment event: 20
- Pull request review event: 15
- Fork event: 1
- Create event: 6
Last Year
- Issues event: 4
- Delete event: 5
- Issue comment event: 4
- Push event: 18
- Pull request event: 7
- Pull request review comment event: 20
- Pull request review event: 15
- Fork event: 1
- Create event: 6
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 3
- Total pull requests: 4
- Average time to close issues: about 20 hours
- Average time to close pull requests: 11 minutes
- Total issue authors: 2
- Total pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 3
- Pull requests: 4
- Average time to close issues: about 20 hours
- Average time to close pull requests: 11 minutes
- Issue authors: 2
- Pull request authors: 1
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- danielandresarcones (4)
- joergfunger (3)
- div-tyg (1)
Pull Request Authors
- danielandresarcones (5)
- joergfunger (2)
Top Labels
Issue Labels
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Packages
- Total packages: 1
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Total downloads:
- pypi 123 last-month
- Total dependent packages: 0
- Total dependent repositories: 1
- Total versions: 34
- Total maintainers: 1
pypi.org: probeye
A general framework for setting up parameter estimation problems.
- Documentation: https://probeye.readthedocs.io/
- License: MIT License
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Latest release: 3.0.4
published over 3 years ago
Rankings
Maintainers (1)
Dependencies
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