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https://arxiv.org/pdf/2207.08200
this paper provides a different way to optimize the priors using distance aware priors.
Maybe i can try to implement it.
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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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### Issue Type
Bug
### Source
binary
### Keras Version
2.16.0
### Custom Code
No
### OS Platform and Distribution
Linux Ubuntu 20.04
### Python version
3.10
### GPU model and memory
_No r…
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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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Bom dia
É possível reabrir e estender o prazo de resposta para um quiz já com respostas. Refiro-me ao quiz da UC de Modelos Apoio à Decisão - " Bayesian Networks". Pretendia que ficasse aberto a resp…
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Automated filtering with machine learning models are an important part of trust and safety on social networks.
Techniques like [naive bayesian filtering](https://en.wikipedia.org/wiki/Naive_Bayes_s…
evanp updated
2 weeks ago
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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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Can we use compound distributions to represent Bayesian networks?
Some possible SymPy extensions:
- Support multiple compounded hyperparameters on one random symbol.
- Support nesting compound …