  In this paper we describe a Bayesian statistical method designed to infer the
magnetic properties of stars observed using high-resolution circular
spectropolarimetry in the context of large surveys. This approach is well
suited for analysing stars for which the stellar rotation period is not known,
and therefore the rotational phases of the observations are ambiguous. The
model assumes that the magnetic observations correspond to a dipole oblique
rotator, a situation commonly encountered in intermediate and high-mass stars.
Using reasonable assumptions regarding the model parameter prior probability
density distributions, the Bayesian algorithm determines the posterior
probability densities corresponding to the surface magnetic field geometry and
strength by performing a comparison between the observed and computed Stokes V
profiles.
  Based on the results of numerical simulations, we conclude that this method
yields a useful estimate of the surface dipole field strength based on a small
number (i.e. 1 or 2) of observations. On the other hand, the method provides
only weak constraints on the dipole geometry. The odds ratio, a parameter
computed by the algorithm that quantifies the relative appropriateness of the
magnetic dipole model versus the non-magnetic model, provides a more sensitive
diagnostic of the presence of weak magnetic signals embedded in noise than
traditional techniques.
  To illustrate the application of the technique to real data, we analyse seven
ESPaDOnS and Narval observations of the early B-type magnetic star LP Ori.
Insufficient information is available to determine the rotational period of the
star and therefore the phase of the data; hence traditional modelling
techniques fail to infer the dipole strength. In contrast, the Bayesian method
allows a robust determination of the dipole polar strength,
$B_d=911^{+138}_{-244}$ G.
