  This work proposes $\mu$GUIDE: a general Bayesian framework to estimate
posterior distributions of tissue microstructure parameters from any given
biophysical model or MRI signal representation, with exemplar demonstration in
diffusion-weighted MRI. Harnessing a new deep learning architecture for
automatic signal feature selection combined with simulation-based inference and
efficient sampling of the posterior distributions, $\mu$GUIDE bypasses the high
computational and time cost of conventional Bayesian approaches and does not
rely on acquisition constraints to define model-specific summary statistics.
The obtained posterior distributions allow to highlight degeneracies present in
the model definition and quantify the uncertainty and ambiguity of the
estimated parameters.
