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The most important change in this version is the new caching feature for compartment models.
This cache is meant to contain values that are constant per volume, to speed up the evaluation of the compartment model for each volume.
The speed-up is dependent on the model, but for AxCaliber and Bingham NODDI the speed-up is about 2~5x.
Added
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- Adds a caching mechanism for caching computations in a compartment model.
- Added a post-sampling callback to add additional results to the sampling output.
- Adds average auto correlation to the sampling post processing.
- Adds default RWM epsilons for the SCAM MCMC algorithm, set to 1e-5 times the initial proposal standard deviation of a parameter.
Other
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- Use nifti.header instead of nifti.get_header() when working with Nibabel.