tensorflow / tensorflow/probability
Bayesian Neural Network hierarchical prior
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Description
The default prior in Convolution3DReparametrization() is tfp.layers.default_multivariate_normal_fn which is an isotrophic Gaussian with mean 0 and standard devitaion1. The posterior is tfp_layers_util.default_mean_field_normal_fn() . Is it possible to specify a hierarchical model for prior and posterior for example, prior~N(0,sigma) and sigma~Gamma(a1,b1)? How can I implement this in Tensorflow probability, may be with tfd.JointDistributionSequential?
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First steps
- Read the whole issue, then the project's contributing guide.
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Research direction
Start by examining the prior and posterior callables named in the issue: tfp.layers.default_multivariate_normal_fn and tfp_layers_util.default_mean_field_normal_fn. Investigate whether JointDistributionSequential can express the requested hierarchical prior and posterior, and define what supported implementation or documentation would count as done.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- tensorflow
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100