Bayesian network: Difference between revisions
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Bayesian networks have applications in machine learning tasks that deal with dependent features. | Bayesian networks have applications in machine learning tasks that deal with dependent features. | ||
An example is | An example is weather forecasting, where weather at each given time is strongly dependent on weather at a previous time. | ||
= Properties = | = Properties = | ||
Revision as of 06:06, 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 weather forecasting, where weather at each given time is strongly dependent on weather at a previous time.
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.
Each node is a conditional probability: Given that the parents happen, what is the probability of the node event happening.
Joint probability
Bayesian networks are primarily used to calculate the joint probability of an event given its dependencies. This can be done with the following formula
By relaxing some assumptions, Naiive Bayes reduces the computational complexity.
