The No-U-Turn Hamiltonian Monte Carlo (HMC) method provide a robust method to capture a posterior distributions with efficient sampling and adaptive step size selection. Capturing the parameter posterior distributions for $\varepsilon = (\hat{y} - y$). This issue surmises the following:

• Integrate a NUTS implementation from a common, well-supported probablistic library
• Build corresponding test suite with code coverage
• Build diagonostics from posterior distribution (i.e. parameter observability) as well as performance-based metrics.