%PDF-1.3 1 0 obj << /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R ] /Type /Pages /Count 8 >> endobj 2 0 obj << /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) /Publisher (Curran Associates) /Language (en\055US) /Created (2007) /Description-Abstract (We consider the estimation problem in Gaussian graphical models with arbitrary structure\056 We analyze the Embedded Trees algorithm\054 which solves a sequence of problems on tractable subgraphs thereby leading to the solution of the estimation problem on an intractable graph\056 Our analysis is based on the recently developed walk\055sum interpretation of Gaussian estimation\056 We show that non\055stationary iterations of the Embedded Trees algorithm using any sequence of subgraphs converge in walk\055summable models\056 Based on walk\055sum calculations\054 we develop adaptive methods that optimize the choice of subgraphs used at each iteration with a view to achieving maximum reduction in error\056 These adaptive procedures provide a significant speedup in convergence over stationary iterative methods\054 and also appear to converge in a larger class of models\056) /Producer (Python PDF Library \055 http\072\057\057pybrary\056net\057pyPdf\057) /Title (Adaptive Embedded Subgraph Algorithms using Walk\055Sum Analysis) /Date (2007) /Type (Conference Proceedings) /firstpage (249) /Book (Advances in Neural Information Processing Systems 20) /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) /Editors (J\056C\056 Platt and D\056 Koller and Y\056 Singer and S\056T\056 Roweis) /Author (Venkat Chandrasekaran\054 Alan S\056 Willsky\054 Jason K\056 Johnson) /lastpage (256) >> endobj 3 0 obj << /Type /Catalog /Pages 1 0 R >> endobj 4 0 obj << /Contents 12 0 R /Rotate 0 /Resources << /Font << /T1_2 13 0 R /T1_3 15 0 R /T1_0 16 0 R /T1_1 17 0 R >> /ProcSet [ /PDF /Text ] >> /CropBox [ 0 0 612 792 ] 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