deletions | additions       diff --git a/Repeating_above_data.tex b/Repeating_above_data.tex  index b9bac2f..7a4155e 100644  --- a/Repeating_above_data.tex  +++ b/Repeating_above_data.tex 
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\begin{center}  \begin{table}  \begin{tabular}{c|c|c|c|c|}  \cline{2-5} \begin{tabular}{c|c|c|c|c|c|}  \cline{2-6}  & \multicolumn{4}{ \multicolumn{5}{  c| }{Process Time (in CPU time)} \\ \cline{2-5} \cline{2-6}  & Our Method & Gibbs $K_1=20$ & Gibbs $K_2=40$ & Gibbs $K_3=60$ $K_3=60$& Gibbs $K_3=80$  \\ \cline{1-5} \cline{1-6}  \multicolumn{1}{ |c| }{median} &15.07 & 6.50&15.05 &43.77 &477.9 &1092.0 &1768.0 &2611.0  \\ \cline{1-5} \cline{1-6}  \multicolumn{1}{ |c| }{mean} &16.05 & 10.13&15.68 & 46.71 &491.3 &1104.0 &1805.0 &2685.0  \\ \cline{1-5} \cline{1-6}  \multicolumn{1}{ |c| }{standard deviation} &4.05&17.79 & 3.57& 9.43 &93.4 &185.0 &270.3 &463.0  \\ \cline{1-5} \cline{1-6}  \end{tabular}\vspace{.2in}  \caption{\label{tab:time} For each test set, the corresponding process time is for a single Markov chain with K-means initial state for Gibbs sampler with pre-chosen number of clusters. The time for our method records the total CPU time for finishing 100 parallel Markov chains for each test set. The medians, means and standard deviations here are taken across all 100 test sets.}  \end{table}