A research notebook for exploring the usage of bayespy
for implementing Gaussian Process Regression Networks.
- Draw graph of all variables, parameters, and hyperparameters for GPRN
- Implement GPRN
- Build GPRN framework in
bayespy
for learning hyperparameters- Translate graph into code
-
Implement update message for$a_j$ as described in the paper -
Implement update for$\theta_f$ -
Implement update for$\theta_W$
- Implement MCMC sampling (possibly using elliptical slice sampling)
- Build a simple API
- Build GPRN framework in
- Obtain data used in the paper
- Test new implementation using data