Robust sampling for weak lensing and clustering analyses with the Dark
  Energy Survey
  Recent cosmological analyses rely on the ability to accurately sample from
high-dimensional posterior distributions. A variety of algorithms have been
applied in the field, but justification of the particular sampler choice and
settings is often lacking. Here we investigate three such samplers to motivate
and validate the algorithm and settings used for the Dark Energy Survey (DES)
analyses of the first 3 years (Y3) of data from combined measurements of weak
lensing and galaxy clustering. We employ the full DES Year 1 likelihood
alongside a much faster approximate likelihood, which enables us to assess the
outcomes from each sampler choice and demonstrate the robustness of our full
results. We find that the ellipsoidal nested sampling algorithm
$\texttt{MultiNest}$ reports inconsistent estimates of the Bayesian evidence
and somewhat narrower parameter credible intervals than the sliced nested
sampling implemented in $\texttt{PolyChord}$. We compare the findings from
$\texttt{MultiNest}$ and $\texttt{PolyChord}$ with parameter inference from the
Metropolis-Hastings algorithm, finding good agreement. We determine that
$\texttt{PolyChord}$ provides a good balance of speed and robustness, and
recommend different settings for testing purposes and final chains for analyses
with DES Y3 data. Our methodology can readily be reproduced to obtain suitable
sampler settings for future surveys.
