Bayesian network: Difference between revisions
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(Created page with "Category:Machine Learning The '''Bayesian network''' is a network probabilistic, graphical model that describes dependencies. = Application = Bayesian networks have applications in machine learning tasks that deal with dependent features. An example is Part-of-speech, where words are grammatically classified in a string. This involves a complex network of dependencies between object, subject, verbs, nouns, etc. that can be modeled and optimized with a Bayesia...") |
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An example is [[Part-of-speech]], where words are grammatically classified in a string. This involves a complex network of dependencies between object, subject, verbs, nouns, etc. that can be modeled and optimized with a Bayesian network. | An example is [[Part-of-speech]], where words are grammatically classified in a string. This involves a complex network of dependencies between object, subject, verbs, nouns, etc. that can be modeled and optimized with a Bayesian network. | ||
= Properties = | |||
== DAG == | |||
Bayesian networks are directed, acyclic [[graph]]s. | |||
Each edge identifies a causal relation, usually temporal: something that happened in the future cannot cause something to happen in the past. As such, the graph is ''acyclic''. | |||
Revision as of 05:51, 24 May 2024
The Bayesian network is a network probabilistic, graphical model that describes dependencies.
Application
Bayesian networks have applications in machine learning tasks that deal with dependent features.
An example is Part-of-speech, where words are grammatically classified in a string. This involves a complex network of dependencies between object, subject, verbs, nouns, etc. that can be modeled and optimized with a Bayesian network.
Properties
DAG
Bayesian networks are directed, acyclic graphs.
Each edge identifies a causal relation, usually temporal: something that happened in the future cannot cause something to happen in the past. As such, the graph is acyclic.
