  We build a framework using tools from Bayesian data analysis to evaluate
models explaining the periodic variations in spin-down and beam-width of PSR
B1828-11. The available data consists of the time averaged spin-down rate,
which displays a distinctive double-peaked modulation, and measurements of the
beam-width. Two concepts exist in the literature that are capable of explaining
these variations; we formulate predictive models from these and quantitatively
compare them. The first concept is phenomenological and stipulates that the
magnetosphere undergoes periodic switching between two meta-stable states as
first suggested by Lyne et al. The second concept, precession, was first
considered as a candidate for the modulation of B1828-11 by Stairs et al.. We
quantitatively compare models built from these concepts using a Bayesian
odds-ratio. Because the phenomenological switching model itself was informed by
this data in the first place, it is difficult to specify appropriate
parameter-space priors that can be trusted for an unbiased model comparison.
Therefore we first perform a parameter estimation using the spin-down data, and
then use the resulting posterior distributions as priors for model comparison
on the beam-width data. We find that a precession model with a simple circular
Gaussian beam geometry fails to appropriately describe the data, while allowing
for a more general beam geometry provides a good fit to the data. The resulting
odds between the precession model (with a general beam geometry) and the
switching model are estimated as $10^{2.7 \pm 0.5}$ in favour of the precession
model.
