When using multiple runs of the same MCMC algorithm (and settings) with bounds, sometimes it will return a 0 acceptance rate and fail, while other runs will work as expected. Our hopeful use case would be to provide R wrappers to your fast MCMC samplers with custom bounded density functions, but the instability has us concerned.
To simplify the issue and provide a hopefully reproducible example, take the HMC example with Eigen in the package documentation. If bounds are added between 1.55 and 50 for both parameters, it fails >10% of the time. But when I increase the lower bound to 1.65, the algorithm almost always fails. Does this have to do with the step-size? Or am I missing something?
When using multiple runs of the same MCMC algorithm (and settings) with bounds, sometimes it will return a 0 acceptance rate and fail, while other runs will work as expected. Our hopeful use case would be to provide R wrappers to your fast MCMC samplers with custom bounded density functions, but the instability has us concerned.
To simplify the issue and provide a hopefully reproducible example, take the HMC example with Eigen in the package documentation. If bounds are added between 1.55 and 50 for both parameters, it fails >10% of the time. But when I increase the lower bound to 1.65, the algorithm almost always fails. Does this have to do with the step-size? Or am I missing something?