SPIDER The Spider Objects

BAYES object

   A=BAYES(C,H) returns a bayes object initialized with density
   estimator C and hyperparameters H. 
   Training will try to fit the estimator C to each class of 
   pattern recognition data. C can be an array of algorithms, one
   for each class.
   Testing will return the class with the highest probability
   estimate, or if passed the empty dataset will generate new 
   class data according to the densities learnt 
    l=50            -- number of data points to generate if asked to generate   
    train_priors=1  -- whether to adjust priors or not according to data
    child=gauss     -- array of underlying density estimators
    prior=1         -- prior probabilities for each class, default equal
    train, test, generate
     get_mean(train(cv({bayes svm}),toy))
     get_mean(train(cv({bayes(gauss('assume="diag_cov"')) svm}),toy))
     gen(bayes({gauss([-1]) gauss([1])}))
     d=gen(bayes({gauss([-1 3]) gauss([0 4]) gauss([1 2])}))

Reference : chapter 2 (Richard O. Duda and Peter E. Hart) Bayesian Decision Theory
Author : Richard O. Duda , Peter E. Hart
Link : http://www.amazon.com/exec/obidos/tg/detail/-/0471056693/002-6279399-2828812?v=glance