> 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

>        Hyperparameters:
>         l=50            -- number of data points to generate if asked to generate
>         train_priors=1  -- whether to adjust priors or not according to data

>        Model:
>         child=gauss     -- array of underlying density estimators
>         prior=1         -- prior probabilities for each class, default equal

>        Methods:
>         train, test, generate

>        Examples:
>          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