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DAM

2008

2008

We consider {0, 1}n as a sample space with a probability measure on it, thus making pseudo-Boolean functions into random variables. We then derive explicit formulas for approximating a pseudo-Boolean random variable by a linear function if the measure is permutation-invariant, and by a function of degree at most k if the measure is a product measure. These formulas generalize results due to Hammer-Holzman and Grabisch-Marichal-Roubens. We also derive a formula for the best faithful linear approximation that extends a result due to Charnes-Golany-Keane-Rousseau concerning generalized Shapley values. We show that a theorem of Hammer-Holzman that states that a pseudo-Boolean function and its best approximation of degree at most k have the same derivatives up to order k does not generalize to this setting for arbitrary probability measures, but does generalize if the probability measure is a product measure. Key words: pseudo-Boolean function, probability measure, pseudo inner product, li...

Related Content

Added |
26 Dec 2010 |

Updated |
26 Dec 2010 |

Type |
Journal |

Year |
2008 |

Where |
DAM |

Authors |
Guoli Ding, Robert F. Lax, Jianhua Chen, Peter P. Chen |

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