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Abstract: A fundamental issue in real-world systems, such as sensor networks, is the selection of observations which most effectively reduce uncertainty. More specifically, we address the long standing problem of nonmyopically selecting the most informative subset of variables in a graphical model. We present the first efficient randomized algorithm providing a constant factor (1-1/e-#) approximation guarantee for any # > 0 with high confidence. (Update)
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BibTeX entry: (Update)
@misc{ carlos-nearoptimal,
author = "Andreas Krause Carlos",
title = "Near-optimal Nonmyopic Value of Information in Graphical Models",
url = "citeseer.ist.psu.edu/751618.html" }
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19 An approximate nonmyopic computation for value of informatio.. (context) - Heckerman, Horvitz et al. - 1993
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8 Selective evidence gathering for diagnostic belief networks - van der Gaag, Wessels - 1993
6 Myopic value of information in influence diagrams - Dittmer, Jensen - 1997
4 Optimal nonmyopic value of information in graphical models -.. (context) - Krause, Guestrin - 2005
4 Learning diagnostic policies from examples by systematic sea.. (context) - Bayer-Zubek - 2004
3 Gaussian processes for active data mining of spatial aggrega.. - Ramakrishnan, Bailey-Kellogg et al. - 2005
2 Near-optimal sensor placements in gaussian processes - Guestrin, Krause et al. - 2005
1 The maximization of submodular functions (context) - Goldengorin, Tijssen et al. - 1999
1 A note on maximizing a submodular set function subject to kn.. (context) - Sviridenko - 2004
1 A note on the budgeted maximization of submodular functions (context) - Krause, Guestrin - 2005
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