anchor.diff:  
   		<p >BayesDB is suitable for analyzing complex, heterogeneous data tables with up to tens of thousands of rows and hundreds of variables. No preprocessing or parameter adjustment is required, though experts can override BayesDB's default assumptions when appropriate.</p>
   
  -		<p >BayesDB's inferences are based in part on <a href="http://probcomp.csail.mit.edu/crosscat">CrossCat</a>, a new, nonparametric Bayesian machine learning method, that automatically estimates the full joint distribution behind arbitrary data tables.</p>
  +		<p> BayesDB assumes that each row of your table is a sample from some fixed population or generative process, and estimates the joint distribution over the columns. BQL then allows you to draw Bayesian inferences about individuals and about the overall population or process. The estimates are currently provided by <a href="http://probcomp.csail.mit.edu/crosscat">CrossCat</a>, a new nonparametric Bayesian method for analyzing high-dimensional data tables.</p>
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  		<a id="Examples" class="anchor">&nbsp;</a><h1>Examples</h1>
  		<p><code>INFER salary FROM mytable WHERE age > 30;</code></p>
  
  		<p >Fill in missing data with the <code>INFER</code> command. Unlike a traditional regression model, where you need to separately train a supervised model for each column you're interested in predicting, <code>INFER</code> statements are flexible and work with any set of columns to predict.</p>
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  <p class="lead">
    Query the probable implications of your tabular data as easily as an SQL database lets you query the data itself.
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