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Efficient estimation exploiting independence constraints

Full Text: bayest.ps.ps PS

There is an obvious way to obtain an unbiased es­timate of the response to a query P ( C = c j E= e ) from a sufficiently large data sample: just use the empirical frequency. Unfortunately, this can have high variance (or even be undefined) when the conditioning event E = e is rare. In some situations, we are able to reduce this variance by exploiting our knowledge about the underlying distribution --- in particular, by using a known set of probabilistic indepedencies amongst the vari­ables, encoded in the structure of a Bayesian be­lief network. The report investigates ways to ex­ploit those independencies when estimating the response to a probabilistic query. In particular, we show a general approach that produces an un­biased estimate with small variance. Of inde­pendent interest, we also present a closed form expression for the (asymptotic) variance of a re­sponse wrt a given structure and sample.

Citation

R. Greiner, D. Schuurmans, C. O'Brien. "Efficient estimation exploiting independence constraints". January 2002.

Keywords: machine learning
Category:  

BibTeX

@incollection{Greiner+al:02,
  author = {Russ Greiner and Dale Schuurmans and Chris O'Brien},
  title = {Efficient estimation exploiting independence constraints},
  year = 2002,
}

Last Updated: March 07, 2007
Submitted by Nelson Loyola

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