  This study presents a proof-of-concept for a novel Bayesian inverse method in
a one-dimensional setting, aimed at proton beam therapy treatment verification.
Our methodology is predicated on a hypothetical scenario wherein strategically
positioned sensors detect prompt-{\gamma}'s emitted from a proton beam when it
interacts with defined layers of tissue. Using this data, we employ a Bayesian
framework to estimate the proton beam's energy deposition profile. We validate
our Bayesian inverse estimations against a closed-form approximation of the
Bragg Peak in a uniform medium and a layered lung tumour.
