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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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Work with multi-layer bayesian neural networks and compare it with more classical methods (ADVI).
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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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**Notebook title**: Variational Inference: Bayesian Neural Networks
**Notebook url**: https://www.pymc.io/projects/examples/en/latest/variational_inference/bayesian_neural_network_advi.html
## Iss…
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Are there any plans for implementing causal Bayesian networks?
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I think that Bayesian neural networks would be a natural successor to our tutorials on Bayesian GLMs.
As far as the neural network library to use, I think [Flux](https://github.com/FluxML/Flux.jl) …
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Thank you very much for your task, I have been following up your research recently, may I ask where is the code related to Bayesian networks? I didn't see the code for the Bayesian network model
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Can we use compound distributions to represent Bayesian networks?
Some possible SymPy extensions:
- Support multiple compounded hyperparameters on one random symbol.
- Support nesting compound …
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Write notebook on bayesian neural networks
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