We investigate the Monte Carlo approach to propagation of experimental
uncertainties within the context of the established "MSTW 2008" global analysis
of parton distribution functions (PDFs) of the proton at next-to-leading order
in the strong coupling. We show that the Monte Carlo approach using replicas of
the original data gives PDF uncertainties in good agreement with the usual
Hessian approach using the standard Delta(chi^2) = 1 criterion, then we explore
potential parameterisation bias by increasing the number of free parameters,
concluding that any parameterisation bias is likely to be small, with the
exception of the valence-quark distributions at low momentum fractions x. We
motivate the need for a larger tolerance, Delta(chi^2) > 1, by making fits to
restricted data sets and idealised consistent or inconsistent pseudodata.
Instead of using data replicas, we alternatively produce PDF sets randomly
distributed according to the covariance matrix of fit parameters including
appropriate tolerance values, then we demonstrate a simpler method to produce
an arbitrary number of random predictions on-the-fly from the existing
eigenvector PDF sets. Finally, as a simple example application, we use Bayesian
reweighting to study the effect of recent LHC data on the lepton charge
asymmetry from W boson decays.Comment: 37 pages, 17 figures. v2: version published in JHEP. Supplementary
material at http://mstwpdf.hepforge.org/random