  High-dimensional reduction methods are powerful tools for describing the main
patterns in big data. One of these methods is the topological data analysis
(TDA), which modeling the shape of the data in terms of topological properties.
This method specifically translates the original data into two-dimensional
system, which is graphically represented via the 'persistence diagram'. The
outliers points on this diagram present the data pattern, whereas the other
points behave as a random noise. In order to determine which points are
significant outliers, replications of the original data set are needed. Once
only one original data is available, replications can be created by fitting a
model for the points on the persistence diagram, and then using the MCMC
methods. One of such model is the RST (Replicating Statistical Topology). In
this paper we suggest a modification of the RST model. Using a simulation
study, we show that the modified RST improves the performance of the RST in
terms of goodness of fit. We use the MCMC Metropolis-Hastings algorithm for
sampling according to the fitted model.
