MUD
Analytical solutions and some associated utility functions for computing Maximal Updated Density (MUD) parameter estimates for Data-Consistent Inversion.
Description
Maximal Updated Density Points are the values which maximize an updated density, analogous to how a MAP (Maximum A-Posteriori) point maximizes a posterior density from Bayesian inversion. Updated densities differ from posteriors in that they are the solution to a different problem which seeks to match the push-forward of the updated density to a specified observed distribution.
Metadata
Release files for mud 0.1.2
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| mud-0.1.2.tar.gz | 15.9 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| mud-0.1.2-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 16.0 MB
Release files / mud-0.1.2.tar.gz
| Download URL | mud-0.1.2.tar.gz |
|---|---|
| Size | 15.9 MB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.7
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Release files / mud-0.1.2-py2.py3-none-any.whl
| Download URL | mud-0.1.2-py2.py3-none-any.whl |
|---|---|
| Size | 60.9 kB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/4.0.2 CPython/3.11.7
|