  Recently, global pulsar timing arrays have released results from searching
for a nano-Hertz gravitational wave background signal. Although there has not
been any definite evidence of the presence of such a signal in residuals of
pulsar timing data yet, with more and improved data in future, a statistically
significant detection is expected to be made. Stochastic algorithms are used to
sample a very large parameter space to infer results from data. In this paper,
we attempt to rule out effects arising from the stochasticity of the sampler in
the inference process. We compare different configurations of nested samplers
and the more commonly used markov chain monte carlo method to sample the pulsar
timing array parameter space and account for times taken by the different
samplers on same data. Although we obtain consistent results on parameters from
different sampling algorithms, we propose two different samplers for robustness
checks on data in the future to account for cross-checks between sampling
methods as well as realistic run-times.
