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   		<a id="About" class="anchor">&nbsp;</a><h1>About</h1>
  -		<p>BayesDB and its sister project, CrossCat, are being developed by Jay Baxter, Dan Lovell, and Vikash Mansinghka at Massuchusetts Institute of Technology and by Pat Shafto and Baxter Eaves at University of Louisville.</p>
  +		<p>BayesDB and its sister project, CrossCat, are being developed by <a href="http://web.mit.edu/jbaxter/www">Jay Baxter</a>, Dan Lovell, and <a href="http://web.mit.edu/vkm/www">Vikash Mansinghka</a> from the MIT <a href="http://probcomp.csail.mit.edu">Probabilistic Computing Project</a> and by <a href="http://shaftolab.com/index.php/people/patrick-shafto/">Pat Shafto</a> and <a href="http://shaftolab.com/index.php/people/baxter-eaves/">Baxter Eaves</a> from the <a href="http://shaftolab.com">Shafto Lab</a> at University of Louisville.</p>
   
   		<p>If you have any comments or questions, please feel free to email us at bayesdb [AT] mit.edu.</p>
   
  
positive.code: In BQL, much like in traditional DBMSs, we see schema design (both choosing how to organize raw data into a table and choosing its types) as an avenue for users to articulate their prior knowledge. Right now the mechanisms for doing so are relatively limited, but we anticipate that this will change. 
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  If it survives, BQL will likely evolve towards becoming a general-purpose probabilistic programming language, perhaps even one like <link>Venture</link> or Church. From this perspective, the current CrossCat backend corresponds to a simple kind of hierarchical Bayesian probabilistic program synthesis, where the class of probabilistic programs it is learning is extremely simple.
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negative.code: </p>
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  	<li><p>CrossCat contains various simpler, standard black box models as hypothesis&mdash;including Dirichlet processs mixtures and a form of naive Bayes. Modulo bugs, this means that with enough inference, CrossCat shouldn't perform essentially worse than these simpler methods, and is likely to perform better.</p></li>
  	<li><p>CrossCat includes many hypotheses that correspond to versions of the claim "I can find no signal in this part of the dataset", and is quite happy to report broad predictive distributions when it is uncertain. Although there might actually be signal that another method could have found, we think this is a good direction to err in. Some versions have been tested with the kinds of small N, high-D, extremely noisy datasets that confuse many standard methods and performed surprisingly well, though we have more empirical work to do there.</p></li>
  	
  	<li><p>There are theoretical results that suggest CrossCat is an asymptotically consistent estimator of the joint distribution. This has actually been proved for Dirichlet process mixture models with various component models, and we do not think the increased flexibility of CrossCat interferes (as N &rarr; infinity, though there is a subtlety if D &rarr; infinity). This roughly means that for a surprisingly broad class of distributions, if you run inference long enough and have enough data, CrossCat will infer a model that predicts equivalently with the 'true' model.</p></li>
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