## Bayesian Data Analysis for Data Mining (2002)

Venue: | In Handbook of Data Mining |

Citations: | 1 - 0 self |

### BibTeX

@INPROCEEDINGS{Madigan02bayesiandata,

author = {David Madigan and Greg Ridgeway},

title = {Bayesian Data Analysis for Data Mining},

booktitle = {In Handbook of Data Mining},

year = {2002},

pages = {103--132},

publisher = {MIT Press}

}

### OpenURL

### Abstract

Introduction The Bayesian approach to data analysis computes conditional probability distribu- tions of quantities of interest (such as future observables) given the observed data. Bayesian analyses usually begin with a .full probability model - a joint probability dis- tribution for all the observable and unobservable quantities under study - and then use Bayes' theorem (Bayes, 1763) to compute the requisite conditional probability distributions (called poster'Joy distributions). The theorem itself is innocuous enough. In its simplest form, if Q denotes a quantity of interest and D denotes data, the theorem states: P(ql D) P(;lq) X P(q)/P(). This theorem prescribes the basis for statistical learning in the probabilistic frame- work. With p(Q) regarded as a probabilistic statement of prior knowledge about Q before obtaining the data D, p(QI D) becomes a revised probabilistic statement of our knowledge about Q in the light of the data (Bernardo and Smith, 1994, p.2). The marginal lik