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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>
   
  
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  <ol>
  	<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 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 standard statistical 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>
  </ol>
  
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  		<a id="FAQ" class="anchor">&nbsp;</a><h1>FAQ</h1>
  		<h2>Who and what is BayesDB for?</h2>
  <p>BayesDB is aimed at:</p>
  <ol>
  	<li><p>People without statistics expertise but who have tabular data and questions they'd like to answer.</p></li>
  	<li><p>People with statistics expertise and data who either have a problem that doesn't fit the standard supervised templates (e.g. the data is too sparse) or who want to analyze a dataset but don't have the time or inclination for custom modeling.</p></li>
  </ol>
  <p>
  The main goal is to broaden access to analytics and increase productivity. We also hope it will increase the reliability of the empirical reasoning performed by non-experts, who currently lack the ability to build good models or critique the simplistic models that they do have access to.
  