  The Bayesian Hurst-Kolmogorov (HK) method estimates the Hurst exponent of a
time series more accurately than the age-old detrended fluctuation analysis
(DFA), especially when the time series is short. However, this advantage comes
at the cost of computation time. The computation time increases exponentially
with $N$, easily exceeding several hours for $N = 1024$, limiting the utility
of the HK method in real-time paradigms, such as biofeedback and brain-computer
interfaces. To address this issue, we have provided data on the estimation
accuracy of $H$ for synthetic time series as a function of \textit{a priori}
known values of $H$, the time series length, and the simulated sample size from
the posterior distribution -- a critical step in the Bayesian estimation
method. The simulated sample from the posterior distribution as small as $n =
25$ suffices to estimate $H$ with reasonable accuracy for a time series as
short as $256$ measurements. Using a larger simulated sample from the posterior
distribution -- i.e., $n > 50$ -- provides only marginal gain in accuracy,
which might not be worth trading off with computational efficiency. We suggest
balancing the simulated sample size from the posterior distribution of $H$ with
the computational resources available to the user, preferring a minimum of $n =
50$ and opting for larger sample sizes based on time and resource constraints
