title: Learning Bayesian Networks from discrete integral values in R
body: <p>I have a dataset that contains integral values that are discrete with a constant interval. I am trying to learn a Bayesian Network. I tried to convert these values into float and used the discretize function from the bnlearn package on R. I am using the interval method, but I need a large number of breaks to be specified and running into errors.</p>
  
  <p>Here is a small sample of the dataset that I am using to learn the Bayesian Network.</p>
  
  <p><code>X Y P1 P2 P3 P4 P5 P6 P7 P8 P9</code> <code>0,20,123,125,117,122,127,112,123,125,116</code> <code>0,21,120,136,129,116,145,125,118,136,125</code> <code>0,22,122,126,129,116,121,129,117,122,127</code> <code>0,23,130,124,127,126,119,123,126,121,124</code> <code>0,27,126,122,124,118,120,128,122,122,125</code></p>
  
  <p>Could someone please tell me how I can retain the individual values of the data while still being able to learn the Bayesian Network.</p>
  
  <p>Thanks Rochan</p>