tensorflow / tensorflow/probability
tfp.mcmc.HamiltonianMonteCarlo: add example how to infer parameters of Bayesian neural network
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- Jupyter Notebook
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Description
Suggestion: add a 3rd example to tfp.mcmc.HamiltonianMonteCarlo showing how to infer the posterior parameters of a Bayesian neural network (e.g. a simple few-layer Keras Sequential model) using Hamiltonian Monte Carlo.
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the tfp.mcmc.HamiltonianMonteCarlo API documentation and its existing examples. Add a third example using a simple Keras Sequential Bayesian neural network with Hamiltonian Monte Carlo to infer posterior parameters. Done means the example is documented and demonstrates the requested inference workflow.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- jupyter-notebook, machine-learning
- Domain
- documentation, machine-learning
- Issue type
- Documentation
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 38/100