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**Submitting author:** @matteodelucchi (Matteo Delucchi)
**Repository:** https://github.com/furrer-lab/abn
**Branch with paper.md** (empty if default branch):
**Version:** 3.1.3
**Editor:** @crvernon…
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### Subject of the issue
Add support for causal inference in Bayesian Networks. It should be a new class accepting all the models on which causal inference can be done (Bayesian Networks and SEM at t…
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Write notebook on bayesian neural networks
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Hi,
Are there any plans for supporting a Dynamic Bayesian Network?
There's a pgmpy module (https://pgmpy.org/models/dbn.html) but it's not nearly as intuitive to use as bnlearn :)
Thanks In a…
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I created a BayesianNetwork and fit the data:
`model = BayesianNetwork(algorithm="chow-liu", max_parents=max_parents)`
`model.fit(data)`
In `fit` method, it calls `_learn_structure` method, ho…
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I know this may be difficult to answer without much information, but I wanted to see if this issue has been seen before, as I had difficulty finding any past instances of my issue.
In running predi…
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Hello Juan,
I found your post "Flax and Numpyro Toy Example" very cool and interesting. As a PYMC user I wanted to see if I could achieve similar results using PYMC. You can see my implementation at …
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- Time 0 model (baseline conditions and time-dependent variables at T=1)
- Time t model (maps time-dependent variables at time t to the same at time t+1) • unroll.markovNetwork(startTime=NULL, stop…
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I think we can do something really cool about converting deterministic code into Bayesian programs. From the data structures that we have we can put on every variable a distribution and then run the n…