  We demonstrate sequential mass inference of a suspended bag of milk powder
from simulated measurements of the vertical force component at the pivot while
the bag is being filled. We compare the predictions of various sequential
inference methods both with and without a physics model to capture the system
dynamics. We find that non-augmented and augmented-state unscented Kalman
filters (UKFs) in conjunction with a physics model of a pendulum of varying
mass and length provide rapid and accurate predictions of the milk powder mass
as a function of time. The UKFs outperform the other method tested - a particle
filter. Moreover, inference methods which incorporate a physics model
outperform equivalent algorithms which do not.
