Result for 035DECCC4BDFDDE406FB227EB141D1B51BD3F3F8

Query result

Key Value
FileName./usr/share/doc/python-pebl-1.0.2/html/learner/greedy.html
FileSize9160
MD59550BB33FB39BA804FA868C5C7547AFE
SHA-1035DECCC4BDFDDE406FB227EB141D1B51BD3F3F8
SHA-256C6B0B00F1FCF5EB7DAD619686179E4B750B121D44647697F31ACE3ABA242CFEC
SSDEEP96:6YXfFYMA/4tD9Wjm7n3zAp6zK9DBlydENgfLy02nxLjw6j0xS2kQ22pubqvU7S1n:XFXAY17nD6WKj986j0xyQ2j+1Udnf6v
TLSHT1B812532A5EE05A3341131BDFD5E51B22B8C2C057E1460CA5B4FC9D5A9F8BD91AB0BE0B
hashlookup:parent-total2
hashlookup:trust60

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Parents (Total: 2)

The searched file hash is included in 2 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
MD5FD4CA8309518CE89B532D93D2D00F359
PackageArchs390
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
PackageRelease9.fc18
PackageVersion1.0.2
SHA-1718E050AE8AD6F830E8F7E7FF31D08CAB2A00AEB
SHA-25626BBEBDBA2AFFDD40145BCF6D8B1A897EA616320510EAD6F7D1B7CC8BB405277
Key Value
MD5680CB2A8BD3C4EB6A760CD2F17D13CB7
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
PackageRelease9.fc18
PackageVersion1.0.2
SHA-1CC718AD0C8B54E0A8037B241D9AA5172EC3C9049
SHA-25672813ACFC93464A7B0F1010778E466A80899A0E6B4FBC5631493852FBACE2583