I tried to create the Bayesian inference based on the information that I found in the following links:
- [From this link: Hyperbolic Secant Distribution]
- How to choose a Bayesian prior
- Prior Choice Recommendations
- Custom distribution can be found here: Creating a Custom Hypersecant distribution
I think I created the mu as Generic weakly informative prior and same for sigma (hopefully 
), but when it comes to creating the fusion part, I keep on coming back to the priors as I include the observed data as distribution data from sensors. Am I right to think and assume that difference in the reuslts is due to my setting up the model for Bayesian fusion? Like the parametrization like target_accept, draws, tune and the priors.