**Basics**

**Benefits**

**Background theories**

**Versus other paradigms**

**Tutorials**

**Open discussions**

**Contacts**

**Basics**

**Benefits**

**Background theories**

**Versus other paradigms**

**Tutorials**

**Open discussions**

**Contacts**

doc:versus:bayesian

Wikipedia : *Bayesian network is a directed acyclic graphs whose nodes represent random variables in the Bayesian sense: they may be observable quantities, latent variables, unknown parameters or hypotheses. Edges represent conditional dependencies; nodes that are not connected represent variables that are conditionally independent of each other. Each node is associated with a probability function that takes as input a particular set of values for the node's parent variables and gives the probability of the variable represented by the node.*

As defined above, in bayesian network each “*nodes represent random variables*” and “*each node is associated with a probability function*”. This mean there is a design of the bayesian network that define exactly every change of values by a process inside the “*probability function*”.

In Evenja it is not a “*function*” that decide to change the data. But the content of the data it self.

The content of the data, “What”, “When” and in which node “Where” they are define the next node.

More details on file evenja_bayesien_EN.pdf to check the difference with the Bayesian approach.

As in the bayesian network, the status of the data can be defined graphically in an “undirected *acyclic graphs*”.

doc/versus/bayesian.txt · Last modified: 2014/09/30 16:47 by fjp

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