# Questions tagged [bayesian-networks]

For questions related to Bayesian networks, which are e.g. used to study causality (or causation) in AI.

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### What does the log-likelihood say and how is it to be interpreted?

Intro I am new to AI and I started with modeling a bayesian network for my AI agent. I learned the parameters using the EM algorithm. Besides the computed conditional probability distributions, the ...
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### Given A and B, C are independent of each other. Given A, B and C, D and E are independent of each other. What is the minimal number of parameters?

Assuming all variables $A, B, C, D,$ and $E$ are random binary variables. I come up with Bayes net: $D \rightarrow B \rightarrow A \leftarrow C \leftarrow E$ which has the minimal number of parameters ...
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### Why are Directed Graphical Models considered ML methods?

Consider the following problem. The probability of being born in countries [1,2,3,4] is given by [a, b, c, d] respectively. This is a categorical problem. Now, assume that the height of a person ...
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### How do I know the matrix dimensions when summing out variable from product of factors?

Figure 14.10 on p. 527 of Norvig and Russell's book "Artificial Intelligence: A Modern Approach" shows: I see how the submatrices are formed by fixing the variable to each value of $A$, ie. ...
• 115
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### Variational Inference: Approximate expected log likelihood via sampling

I'm working my way through a simple variational inference from scratch. For that, I assume that z denotes the probability of a coin showing ...