Are there any packages associated with Bayesian inference that provide methods to take on model inadequacy, AKA structural error, model bias, etc.? I’m particularly interested in applications to Bayesian parameter estimation and uncertainty quantification in systems of differential equations. Brynjarsdottir and O’Hagan, 2014 give a good overview of the issue and its importance for scientific simulators to which I am referring. For a more recent treatment, see Sargsyan, et al., 2018. Thanks in advance if you have any suggestions.
Yes, take a look at [2012.07244] Bayesian Neural Ordinary Differential Equations
Code examples in Bayesian Neural ODEs: NUTS · DiffEqFlux.jl
Very cool, thanks for these! I’m curious to see how these methods compare to the “non-intrusive PCE” approach described in Sargsyan.