anchor.diff:  </p>
   <h2>How mature is BayesDB?</h2>
   <div class="alert alert-danger">
  -<p><B>WARNING:</B> BayesDB is rapidly-evolving, <B>alpha-quality</B> research software. Some key ideas behind its design and implementation have yet to be published, documentation is currently sparse, our release engineering is rough. We are sure there are critical bugs everywhere, including in the inference backend.</p></div>
  -<p>
  -We are also humbled and excited by the interest in the project, and are working to improve the situation. If you have feature requests or ideas for applications, please let us know.
  -</p>
  -<p>
  -Also, if you think you might be interested in alpha testing, collaborating, or contributing, please write us! We'd love to hear from you at bayesdb [AT] mit.edu.
  +<p><B>WARNING:</B> BayesDB is rapidly-evolving, <B>alpha-quality</B> research software. Some key ideas behind its design and implementation have yet to be published, documentation is currently sparse, our release engineering is rough, and there may well be front-end and back-end bugs.</p></div>
  +<p>
  +We are humbled and excited by the interest in the project, and are working hard to improve BayesDB's maturity
  +</p>
  +<p>
  +Please send us your application ideas, bug reports, and feature requests. Also, if you think you might be interested in alpha testing, collaborating, or contributing, please write us! We'd love to hear from you at bayesdb [AT] mit.edu.
   </p>
   <h2>What kinds of data and problems is it good for?</h2>
   <p>Versions of BayesDB (and CrossCat) have been tried on datasets including surveys, performance metrics for hospitals, branches of nonprofits, econometric tables, feature vectors extracted from text/images, voting records, and gene expression microarrays. 
  
positive.code: 
  		<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>
  	</div>
  </div>
  
  
negative.code: 
  <h2>Why do you call it BayesDB? How is it Bayesian?</h2>
  <p>
  Some Bayesian priors are about precisely expressing ignorance. This is what CrossCat is doing inside BayesDB: encoding a very broad prior over generative processes that admits possibilities ranging from near determinism to unpredictability.
  </p>
  <p>
  That said, BQL does allow users to encode some of their prior knowledge, via schema design. Currently this means deciding what the columns and rows in the table are, and what the datatypes are for each column. Going forward, we plan to support additional user-level configurability.
  </p>
  <p>
  Our current BayesDB implementation takes this idea literally. A single probabilistic model, called CrossCat, is estimated from the data, where (ignoring any bugs) each sample produced by <code>ANALYZE</code> is independently drawn from a distribution between the CrossCat prior and the true posterior given the data in the table. The more runtime you give <code>ANALYZE</code>, the closer to the true posterior BayesDB becomes. The more samples you get, the more accurately you can resolve posterior uncertainty.
  </p>
  
  