Result for 015F3A79BD3AADD1BE19ED19EB6F0DAD6F7EA040

Query result

Key Value
FileName./usr/share/doc/python-pebl-1.0.2/html/searchindex.js
FileSize25075
MD5B51E0D822E163680B2E84E46B5F0EC06
SHA-1015F3A79BD3AADD1BE19ED19EB6F0DAD6F7EA040
SHA-25687EFDDC4CEB56F21D8B3A1FCA87D1A0B6C178AE0F2A4B632217CBF9766F244ED
SSDEEP384:t619JJPzB/GDTXGyQHEZzLEvI2uXUCMx96wdXF1PObl4EBw86gf9y:69JJPzBGQkZ92ukF95JOyWwVgf9y
TLSHT153B264670A650F7BB29AC5DAEA8463C81651B058F07E0450DFB885F9A14DBCB343EF1B
hashlookup:parent-total1
hashlookup:trust55

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Parents (Total: 1)

The searched file hash is included in 1 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5C703F501977D6200CDB1ED253EDA8EAA
PackageArchs390x
PackageDescriptionPebl is a python library and command line application for learning the structure of a Bayesian network given prior knowledge and observations. Pebl includes the following features: - can learn with observational and interventional data - handles missing values and hidden variables using exact and heuristic methods - provides several learning algorithms; makes creating new ones simple - has facilities for transparent parallel execution using several cluster/grid resources - calculates edge marginals and consensus networks - presents results in a variety of formats
PackageMaintainerFedora Project
PackageNamepython-pebl
PackageRelease7.fc17
PackageVersion1.0.2
SHA-179F4C780FB1BF6C4FF42A453E79DBBD576ED38E4
SHA-2569E2159553FB929FF0D9D0C5A1F71175CFE5CC56A576381607D8CD8861E56E2EA