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
• Obtain data used in the paper
• Test new implementation using data