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We build a framework using tools from Bayesian data analysis to evaluatemodels explaining the periodic variations in spin-down and beam-width of PSRB1828-11. The available data consists of the time averaged spin-down rate,which displays a distinctive double-peaked modulation, and measurements of thebeam-width. Two concepts exist in the literature that are capable of explainingthese variations; we formulate predictive models from these and quantitativelycompare them. The first concept is phenomenological and stipulates that themagnetosphere undergoes periodic switching between two meta-stable states asfirst suggested by Lyne et al. The second concept, precession, was firstconsidered as a candidate for the modulation of B1828-11 by Stairs et al.. Wequantitatively compare models built from these concepts using a Bayesianodds-ratio. Because the phenomenological switching model itself was informed bythis data in the first place, it is difficult to specify appropriateparameter-space priors that can be trusted for an unbiased model comparison.Therefore we first perform a parameter estimation using the spin-down data, andthen use the resulting posterior distributions as priors for model comparisonon the beam-width data. We find that a precession model with a simple circularGaussian beam geometry fails to appropriately describe the data, while allowingfor a more general beam geometry provides a good fit to the data. The resultingodds between the precession model (with a general beam geometry) and theswitching model are estimated as $10^{2.7 \pm 0.5}$ in favour of the precessionmodel.

Comparing models of the periodic variations in spin-down and beam-width for PSR B1828-11