Thesis
of Michael Pilosov
The text below is merely a placeholder taken from the MUD repo.
Analytical solutions and some associated utility functions for computing maximal updated density points 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.
More about the differences here...
What does this package include?
Note
This project has been set up using PyScaffold 3.2.3. For details and usage information on PyScaffold see https://pyscaffold.org/.
1 maintainer
Metadata
1 maintainer
Release files for dataconsistent 0.0.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dataconsistent-0.0.1.tar.gz | 1.6 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dataconsistent-0.0.1-py2.py3-none-any.whl | Python 2, Python 3 | none | any | Details |
Total release size: 1.6 MB
Release files / dataconsistent-0.0.1.tar.gz
| Download URL | dataconsistent-0.0.1.tar.gz |
|---|---|
| Size | 1.6 MB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.8.5
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Release files / dataconsistent-0.0.1-py2.py3-none-any.whl
| Download URL | dataconsistent-0.0.1-py2.py3-none-any.whl |
|---|---|
| Size | 3.3 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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twine/3.2.0 pkginfo/1.5.0.1 requests/2.24.0 setuptools/49.2.0 requests-toolbelt/0.9.1 tqdm/4.48.0 CPython/3.8.5
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