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作者： Albrecht, Stefano V. Barreto, André M. S. Braziunas, Darius Buckeridge, David L. Cuayáhuitl, Heriberto Dethlefs, Nina Endres, Markus Farahmand, Amir-Massoud Fox, Mark Frommberger, Lutz Ganzfried, Sam Guillet, Sébastien Gil, Yolanda Hunter, Lawrence E. Jhala, Arnav Kersting, Kristian Konidaris, George Lecue, Freddy McIlraith, Sheila Natarajan, Sriraam Noorian, Zeinab Poole, David Ronfard, Rémi Saffiotti, Alessandro Shaban-Nejad, Arash Srivastava, Biplav Tesauro, Gerald Uceda-Sosa, Rosario Van Den Broeck, Guy Van Otterlo, Martijn Wallace, Byron C. Weng, Paul Wiens, Jenna Zhang, Jie School of Informatics University of Edinburgh United Kingdom Department of Applied and Computational Mathematics Brazilian National Laboratory Brazil Big Data Group Kobo Inc. Brazil Department of Epidemiology and Biostatistics McGill University Canada School of Mathematical and Computer Sciences Heriot-Watt University in Edinburgh United Kingdom University of Augsburg Germany Carnegie Mellon University United States University of Toronto Canada Cognitive Systems Group University of Bremen Germany LIARA UQAC Chicoutimi Canada Information Sciences Institute University of Southern California Computer Science Department United States Center for Computational Pharmacology University of Colorado School of Medicine United States Department of Computational Media University of California Santa Cruz United States Computer Science Department Technical University of Dortmund Germany MIT Computer Science and Artificial Intelligence Laboratory CambridgeMA United States IBM Research - Smarter Cities Technology Centre Dubline Ireland School of Informatics and Computing Indiana University United States Department of Computer Science University of Saskatchewan Canada University of British Columbia Canada INRIA University of Grenoble France Orebro University Sweden McGill Clinical and Health Informatics Group McGill University Canada IBM Research United States Cognitive Computing Department IBM Research Yorktown HeightsNY United States Computer Science Department Katholieke Universiteit Leuven Belgium Cognitive Artificial Intelligence Department Radboud University Nijmegen Netherlands University of Texas Austin United States Pierre and Marie Curie University Paris France University of Michigan United States School of Computer Engineering Nanyang Technological University Singapore

The AAAI-14 Workshop program was held Sunday and Monday, July 27-28, 2014, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Artificial Intelligence and Robotics;Artificial Intelligence Applied to Assistive Technologies and Smart Environments;Cognitive Computing for Augmented Human Intelligence;Computer Poker and Imperfect Information;Discovery Informatics;Incentives and Trust in Electronic Communities;Intelligent Cinematography and Editing;Machine Learning for Interactive systems: Bridging the Gap Between Perception, Action, and Communication;Modern Artificial Intelligence for Health Analytics;Multiagent Interaction Without Prior Coordination;Multidisciplinary Workshop on Advances in Preference Handling;Semantic Cities - Beyond Open data to Models, Standards, and Reasoning;Sequential Decision Making with Big data;Statistical Relational AI;and the World Wide Web and Public Health Intelligence. This article presents short summaries of those events. © 2015, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Decision making

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Highlights from the 11th ISCB Student Council Symposium 2015. Dublin, Ireland. 10 July 2015

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BMC bioinformatics 2016年第3期17 Suppl 3卷 95页

作者： Katie Wilkins Mehedi Hassan Margherita Francescatto Jakob Jespersen R. Gonzalo Parra Bart Cuypers Dan DeBlasio Alexander Junge Anupama Jigisha Farzana Rahman Griet Laenen Sander Willems Lieven Thorrez Yves Moreau Nagarajan Raju Sonia Pankaj Chothani C. Ramakrishnan Masakazu Sekijima M. Michael Gromiha Paddy J Slator Nigel J Burroughs Przemysław Szałaj Zhonghui Tang Paul Michalski Oskar Luo Xingwang Li Yijun Ruan Dariusz Plewczynski Giulia Fiscon Emanuel Weitschek Massimo Ciccozzi Paola Bertolazzi Giovanni Felici Pieter Meysman Manu Vanaerschot Maya Berg Hideo Imamura Jean-Claude Dujardin Kris Laukens Westa Domanova James R. Krycer Rima Chaudhuri Pengyi Yang Fatemeh Vafaee Daniel J. Fazakerley Sean J. Humphrey David E. James Zdenka Kuncic Plant Pathology and Plant-Microbe Biology Section School of Integrative Plant Science Cornell University Ithaca USA Graduate Field of Computational Biology Cornell University Ithaca USA School of Computing & Mathematics University of South Wales Cardiff UK Department of Genome Biology for Neurodegenerative Diseases German Center for Neurodegenerative Diseases (DZNE) within the Helmholtz Association ᅟ Germany Department of Surgery Massachusetts General Hospital The Broad Institute of MIT and Harvard Boston USA Department of Systems Biology Technical University of Denmark Kemitorvet Denmark Protein Physiology Lab Facultad de Ciencias Exactas y Naturales Universidad de Buenos Aires Buenos Aires Argentina Molecular Parasitology Unit (MPU) Institute of Tropical Medicine Antwerp Belgium Advanced Database Research and Modeling (ADReM) research group University of Antwerp Antwerpen Belgium Department of Computer Science University of Arizona Tucson USA Department of Veterinary Clinical and Animal Sciences Center for non-coding RNA in Technology and Health University of Copenhagen Copenhagen Denmark University College Dublin Dublin Ireland Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Minds Medical IT Department Leuven Belgium Scientific Institute of Public Health (WIV-ISP) Platform of Biotechnology and Molecular Biology (PBB) Brussels Belgium Department of Development and Regeneration @ Kulak KU Leuven Kortrijk Belgium Department of Biotechnology Bhupat and Jyoti Metha School of Biosciences Indian Institute of Technology Madras Chennai India Philips Research North America Briarcliff Manor USA Global Scientific Information and Computing Center (GSIC) Tokyo Institute of Technology Tokyo Japan Systems Biology Centre University of Warwick Senate House Coventry UK Systems Biology Doctoral Training Centre University of Warwick Senate House Coventry UK Center for B

A1 Highlights from the eleventh ISCB Student Council Symposium 2015 Katie Wilkins, Mehedi Hassan, Margherita Francescatto, Jakob Jespersen, R. Gonzalo Parra, Bart Cuypers, Dan DeBlasio, Alexander Junge, Anupama Jigisha, Farzana Rahman O1 Prioritizing a drug’s targets using both gene expression and structural similarity Griet Laenen, Sander Willems, Lieven Thorrez, Yves Moreau O2 Organism specific protein-RNA recognition: A computational analysis of protein-RNA complex structures from different organisms Nagarajan Raju, Sonia Pankaj Chothani, C. Ramakrishnan, Masakazu Sekijima; M. Michael Gromiha O3 Detection of Heterogeneity in Single Particle Tracking Trajectories Paddy J Slator, Nigel J Burroughs O4 3D-NOME: 3D NucleOme Multiscale Engine for data-driven modeling of three-dimensional genome architecture Przemysław Szałaj, Zhonghui Tang, Paul Michalski, Oskar Luo, Xingwang Li, Yijun Ruan, Dariusz Plewczynski O5 A novel feature selection method to extract multiple adjacent solutions for viral genomic sequences classification Giulia Fiscon, Emanuel Weitschek, Massimo Ciccozzi, Paola Bertolazzi, Giovanni Felici O6 A systems Biology Compendium for Leishmania donovani Bart Cuypers, Pieter Meysman, Manu Vanaerschot, Maya Berg, Hideo Imamura, Jean-Claude Dujardin, Kris Laukens O7 Unravelling signal coordination from large scale phosphorylation kinetic data Westa Domanova, James R. Krycer, Rima Chaudhuri, Pengyi Yang, Fatemeh Vafaee, Daniel J. Fazakerley, Sean J. Humphrey, David E. James, Zdenka Kuncic

关键词： Visceral Leishmaniasis Feature Selection Method Frequent Itemset Mining Leishmania Donovani Student Council

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Coevolutionary modeling in network formation

Coevolutionary modeling in network formation

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IEEE Global Conference on Signal and Information Processing (GlobalSIP)

作者： Ibrahim Al-Shyoukh Georgios Chasparis Jeff S. Shamma Georgia Institute of Technology Atlanta GA US Department of Data Analysis Systems Software Competence Center Hagenberg GmbH Hagenberg Austria CEMSE Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA

ISBN: (纸本)9781479970896

Network coevolution, the process of network topology evolution in feedback with dynamical processes over the network nodes, is a common feature of many engineered and natural networks. In such settings, the change in network topology occurs at a comparable time scale to nodal dynamics. Coevolutionary modeling offers the possibility to better understand how and why network structures emerge. For example, social networks can exhibit a variety of structures, ranging from almost uniform to scale-free degree distributions. While current models of network formation can reproduce these structures, coevolutionary modeling can offer a better understanding of the underlying dynamics. This paper presents an overview of recent work on coevolutionary models of network formation, with an emphasis on the following three settings: (i) dynamic flow of benefits and costs, (ii) transient link establishment costs, and (iii) latent preferential attachment.

关键词： Network topology Biological system modeling Economics Topology Nash equilibrium Educational institutions Cost function

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Erratum to: Methods for evaluating medical tests and biomarkers

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Diagnostic and prognostic research 2017年第1期1卷 11页

作者： Gowri Gopalakrishna Miranda Langendam Rob Scholten Patrick Bossuyt Mariska Leeflang Anna Noel-Storr James Thomas Iain Marshall Byron Wallace Penny Whiting Clare Davenport Gowri GopalaKrishna Isabel de Salis Sue Mallett Robert Wolff Richard Riley Marie Westwood Jos Kleinen Gary Collins Hans Reitsma Karel Moons Antonia Zapf Annika Hoyer Katharina Kramer Oliver Kuss J Ensor J J Deeks E C Martin R D Riley Gerta Rücker Susanne Steinhauser Martin Schumacher Joie Ensor Kym Snell Brian Willis Thomas Debray Jon Deeks Lavinia Ferrante di Ruffano Sian Taylor-Phillips Chris Hyde Stuart A Taylor Gauraang Batnagar Lavinia Ferrante Di Ruffano Farah Seedat Aileen Clarke Sarah Byron Frances Nixon Rebecca Albrow Thomas Walker Carla Deakin Zhivko Zhelev Harriet Hunt Yaling Yang Lucy Abel James Buchanan Thomas Fanshawe Bethany Shinkins Laure Wynants Jan Verbakel Sabine Van Huffel Dirk Timmerman Ben Van Calster Aeliko Zwinderman Jason Oke Jack O'Sullivan Rafael Perera Brian Nicholson Hannah L Bromley Tracy E Roberts Adele Francis Denniis Petrie G Bruce Mann Kinga Malottki Holly Smith Lucinda Billingham Alice Sitch Oke Gerke Mie Holm-Vilstrup Eivind Antonsen Segtnan Ulrich Halekoh Poul Flemming Høilund-Carlsen Bernard G Francq Jac Dinnes Julie Parkes Walter Gregory Jenny Hewison Doug Altman William Rosenberg Peter Selby Julien Asselineau Paul Perez Aïssatou Paye Emilie Bessede Cécile Proust-Lima Christiana Naaktgeboren Joris de Groot Anne Rutjes Johannes Reitsma Emmanuel Ogundimu Jonathan Cook Yannick Le Manach Yvonne Vergouwe Romin Pajouheshnia Rolf Groenwold Karen Moons Linda Peelen Daan Nieboer Bavo De Cock Micael J Pencina Ewout W Steyerberg Jennifer Cooper Nick Parsons Chris Stinton Steve Smith Andy Dickens Rachel Jordan Alexandra Enocson David Fitzmaurice Peymane Adab Charles Boachie Gaj Vidmar Karoline Freeman Martin Connock Rachel Court Carl Moons Jessica Harris Andrew Mumford Zoe Plummer Kurtis Lee Barnaby Reeves Chris Rogers Veerle Verheyden Gianni D Angelini Gavin J Murphy Jeremy Huddy Melody Ni Katherine Good Graham Cooke 1Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 2Cochrane Netherlands Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht Netherlands. 234Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 49Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 3Cochrane Dementia and Cognitive Improvement Group University of Oxford Oxford UK. 4EPPI-Centre Department of Social Science University College London London UK. 5Division of Health and Social Care Research King's College London London UK. 6College of Computer and Information Science Northeastern University Boston USA. 13University Hospitals Bristol NHS Foundation Trust School of Social and Community Medicine Bristol UK. 275Institute of Applied Health Research University of Birmingham Birmingham UK. 9Department of Clinical Epidemiology Biostatistics & Bioinformatics Academic Medical Center University of Amsterdam Amsterdam The Netherlands. 10School of Social and Community Medicine University of Bristol Bristol UK. 179Institute of Applied Health Research University of Birmingham Birmingham UK. 12Kleijnen Systematic Reviews Ltd York UK. 25Research Institute for Primary Care and Health Sciences Keele University Keele UK. 188Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford Oxford UK. 191Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 237Julius Center for Health Sciences and Primary Care University Medical Center Utrecht 3584 CG Utrecht Netherlands. 18Department of Medical Statistics University Medical Center Göttingen Göttingen Germany. 19Institute for B

[This corrects the article DOI: 10.1186/s41512-016-0001-y.].

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ESICM LIVES 2016: part one Milan, Italy. 1-5 October 2016 Abstracts

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INTENSIVE CARE MEDICINE EXPERIMENTAL 2016年第SUPPL 1期4卷 1-1页

作者： [Anonymous] Academic Medical Center University of Amsterdam Amsterdam Netherlands UMCU Utrecht Netherlands Autonomous University of Barcelona Barcelona Spain Hospital São Francisco Xavier Lisbon Portugal CHU Henri Mondor Medical ICU Créteil France Université de Paris Est-Créteil Groupe de Recherche Clinique CARMAS Créteil France Hôpital Tenon APHP Medical and Surgical ICU Paris France Université Paris Diderot Sorbonne Paris Cité UMR 1153 Paris France Interdepartmental Division of Critical Care University of Toronto Toronto Canada University Health Network University of Toronto Critical Care Medicine Toronto Canada University of Milan - Bicocca Health Sciences School of Medicine and Surgery Monza Italy St Michael’s Hospital University of Toronto Anesthesia Toronto Canada CHU d’Angers Medical ICU Angers France Interdepartmental Division of Critical Care University of Toronto Saint Michael’s Hospital and Keenan Research Centre Toronto Canada University of Birmingham Institute for Inflammation and Ageing Birmingham UK University of Sheffield Mathematics and Statistics Help Centre Sheffield UK Heart of England NHS Foundation Trust Intensive Care Medicine Birmingham UK Department of Surgery University Hospitals Birmingham NHS Trust Birmingham UK University Hospitals Birmingham NHS Trust Anaesthesia and Critical Care Medicine Birmingham UK Queen’s University of Belfast Wellcome-Wolfson Institute for Experimental Medicine Belfast UK Royal Victoria Hospital Intensive Care Medicine Belfast UK University of Warwick Warwick Clinical Trials Unit Coventry UK University of Bari ‘Aldo Moro’ Bari Italy Hospital Israelita Albert Einstein São Paulo Brazil Mayo Clinic Jacksonville USA Mahidol University Faculty of Tropical Medicine Bangkok Thailand University Medical Center Utrecht Intensive Care Utrecht Netherlands University Medical Center Utrecht Julius Center for Health Sciences and Primary Care Utrecht Netherlands Academic Medical Center University of Amste

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Statistical evaluation of forecasts

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Physical Review E 2014年第2期90卷 022133-022133页

作者： Malenka Mader Wolfgang Mader Bruce J. Gluckman Jens Timmer Björn Schelter Department of Neuropediatrics and Muscular Disease University Medical Center of Freiburg Freiburg Germany Freiburg Center for Data Analysis and Modeling (FDM) University of Freiburg Freiburg Germany Institute for Physics University of Freiburg Freiburg Germany Center for Neural Engineering Pennsylvania State University State College Pennsylvania 16801 USA Department of Engineering Science and Mechanics Pennsylvania State University State College Pennsylvania 16801 USA Department of Neurosurgery Pennsylvania State University Hershey Pennsylvania 17033 USA Department of Biomedical Engineering Pennsylvania State University State College Pennsylvania 16801 USA BIOSS Center for Biological Signalling Studies University of Freiburg Freiburg Germany Institute for Complex Systems and Mathematical Biology University of Aberdeen Aberdeen UK

Reliable forecasts of extreme but rare events, such as earthquakes, financial crashes, and epileptic seizures, would render interventions and precautions possible. Therefore, forecasting methods have been developed which intend to raise an alarm if an extreme event is about to occur. In order to statistically validate the performance of a prediction system, it must be compared to the performance of a random predictor, which raises alarms independent of the events. Such a random predictor can be obtained by bootstrapping or analytically. We propose an analytic statistical framework which, in contrast to conventional methods, allows for validating independently the sensitivity and specificity of a forecasting method. Moreover, our method accounts for the periods during which an event has to remain absent or occur after a respective forecast.

关键词： EARTHQUAKES FINANCIAL crises EPILEPSY BINOMIAL distribution CONTINGENCY tables

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CORRIGENDUM: "A visualization for the dynamic behaviors of the mixture of water mass for northwestern pacific near Japan"

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International Journal of Modeling, Simulation, and Scientific Computing 2015年第2期6卷

作者： Kun Zhao Satoshi Nakada Naohisa Sakamoto Koji Koyamada Chandrajit Bajaj Yoichi Ishikawa Toshiyuki Awaji Teiji In Sei-Ichi Saitoh Graduate School of Engineering Kyoto University Kyoto Kyoto Prefecture 606-8501 Japan Institute for the Promotion of Excellence in Higher Education Kyoto University Kyoto Kyoto Prefecture 606-8501 Japan Department of Computer Science The Institute for Computational Engineering and Sciences University of Texas at Austin Austin Texas 78712 USA Department of Geophysics Division of Earth and Planetary Sciences Kyoto University Kyoto Kyoto Prefecture 606-8501 Japan Data Research Center for Marine-Earth Sciences Japan Agency for Marine-Earth Science and Technology Yokosuka Kanakawa Prefecture 237-0061 Japan Japan Marine Science Foundation Tokyo Tokyo Metropolis 110-0008 Japan Laboratory of Marine Bioresource and Environment Sensing Faculty of Fisheries Sciences Hokkaido University Sapporo Hokkaido Prefecture 060-0808 Japan

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A VASUALIZATION FOR THE DYNAMIC BEHAVIORS OF THE MIXTURE OF WATER MASS FOR NORTHWESTERN PACIFIC NEAR JAPAN

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International Journal of Modeling, Simulation, and Scientific Computing 2013年第S01期4卷 86-103页

作者： KUN ZHAO SATOSHI NAKADA NAOHISA SAKAMOTO KOJI KOYAMADA CHANDRAJIT BAJAJ YOICHI ISHIKAWA TOSHIYUKI AWAJI TEIJI IN SEI-ICHI SAITOH Graduate School of Engineering Kyoto University KyotoKyoto Prefecture606-8501Japan Institute for the Promotion of Excellence in Higher Education Kyoto University KyotoKyoto Prefecture606-8501Japan Department of Computer Science The Institute for Computational Engineering and Sciences University of Texas at Austin AustinTexas78712USA Department of Geophysics Division of Earth and Planetary Sciences Kyoto University KyotoKyoto Prefecture606-8501Japan Data Research Center for Marine-Earth Sciences Japan Agency for Marine-Earth Science and Technology Yokosuka Kanakawa Prefecture237-0061Japan Japan Marine Science Foundation Tokyo Tokyo Metropolis110-0008Japan Laboratory of Marine Bioresource and Environment Sensing Faculty of Fisheries Sciences Hokkaido University SapporoHokkaido Prefecture060-0808Japan

Recent studies are focusing on the distribution of water mass because the mixture region of water mass is highly related to the rich fishing grounds[Yasuda I.,Watanabe Y.,***.3(3):172–181,1994].Due to the large data size and time-varying property,efficient exploration and visualization of the ocean data is always extremely *** extract the dynamic behaviors of the water mass and its mixture from a large-scale simulated ocean dataset,we developed an efficient visualization system by applying our volume compression method and our volume rendering *** system allows us to investigate the time-varying distributions of ocean physical properties,additionally from the user’s perspective and *** the experiments,we show the generality and expressiveness by applying our system for single-and multi-property visualizations to find some significant ocean water ***,we could obtain a clear visualization result to show the dynamic behaviors of the mixture of water mass for simulation data regarding a location in the northwestern Pacific near Japan.

关键词： Oceanography water mass visualization.

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ERRATUM: 'THE SECOND CATALOG OF ACTIVE GALACTIC NUCLEI DETECTED BY THE FERMI LARGE AREA TELESCOPE' (2011, ApJ, 743, 171)

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The Astrophysical Journal 2015年第1期806卷

作者： M. Ackermann M. Ajello A. Allafort E. Antolini W. B. Atwood M. Axelsson L. Baldini J. Ballet G. Barbiellini D. Bastieri K. Bechtol R. Bellazzini B. Berenji R. D. Blandford E. D. Bloom E. Bonamente A. W. Borgland E. Bottacini A. Bouvier J. Bregeon M. Brigida P. Bruel R. Buehler T. H. Burnett S. Buson G. A. Caliandro R. A. Cameron P. A. Caraveo J. M. Casandjian E. Cavazzuti C. Cecchi E. Charles C. C. Cheung J. Chiang S. Ciprini R. Claus J. Cohen-Tanugi J. Conrad L. Costamante S. Cutini A. de Angelis F. de Palma C. D. Dermer S. W. Digel E. do Couto e Silva P. S. Drell R. Dubois L. Escande C. Favuzzi S. J. Fegan E. C. Ferrara J. Finke W. B. Focke P. Fortin M. Frailis Y. Fukazawa S. Funk P. Fusco F. Gargano D. Gasparrini N. Gehrels S. Germani B. Giebels N. Giglietto P. Giommi F. Giordano M. Giroletti T. Glanzman G. Godfrey I. A. Grenier J. E. Grove S. Guiriec M. Gustafsson D. Hadasch M. Hayashida E. Hays S. E. Healey D. Horan X. Hou R. E. Hughes G. Iafrate G. Jóhannesson A. S. Johnson W. N. Johnson T. Kamae H. Katagiri J. Kataoka J. Knödlseder M. Kuss J. Lande S. Larsson L. Latronico F. Longo F. Loparco B. Lott M. N. Lovellette P. Lubrano G. M. Madejski M. N. Mazziotta W. McConville J. E. McEnery P. F. Michelson W. Mitthumsiri T. Mizuno A. A. Moiseev C. Monte M. E. Monzani E. Moretti A. Morselli I. V. Moskalenko S. Murgia T. Nakamori M. Naumann-Godo P. L. Nolan J. P. Norris E. Nuss M. Ohno T. Ohsugi A. Okumura N. Omodei M. Orienti E. Orlando J. F. Ormes M. Ozaki D. Paneque D. Parent M. Pesce- Rollins M. Pierbattista S. Piranomonte F. Piron G. Pivato T. A. Porter S. Rainò R. Rando M. Razzano S. Razzaque A. Reimer O. Reimer S. Ritz L. S. Rochester R. W. Romani M. Roth D. A. Sanchez C. Sbarra J. D. Scargle T. L. Schalk C. SGRO M. S. Shaw E. J. Siskind G. Spandre P. Spinelli A. W. Strong D. J. Suson H. Tajima H. Takahashi T. Takahashi T. Tanaka J. G. Thayer J. B. Thayer D. J. Thompson L. Tibaldo M. Tinivella D. F. Torres G. Tosti E. Troja Y. Uchiyama J. Vandenbroucke V. Vasileiou G. Vianello V. Vitale A. P. Waite E. Walla W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics and SLAC National Accelerator Laboratory Stanford University Stanford CA 94305 USA Istituto Nazionale di Fisica Nucleare Sezione di Perugia I-06123 Perugia Italy Dipartimento di Fisica Università degli Studi di Perugia I-06123 Perugia Italy Santa Cruz Institute for Particle Physics Department of Physics and Department of Astronomy and Astrophysics University of California at Santa Cruz Santa Cruz CA 95064 USA Department of Astronomy Stockholm University SE-106 91 Stockholm Sweden The Oskar Klein Centre for Cosmoparticle Physics AlbaNova SE-106 91 Stockholm Sweden Department of Physics Royal Institute of Technology (KTH) AlbaNova SE-106 91 Stockholm Sweden Istituto Nazionale di Fisica Nucleare Sezione di Pisa I-56127 Pisa Italy Laboratoire AIM CEA-IRFU/CNRS/Université Paris Diderot Service d'Astrophysique CEA Saclay 91191 Gif sur Yvette France Istituto Nazionale di Fisica Nucleare Sezione di Trieste I-34127 Trieste Italy Dipartimento di Fisica Università di Trieste I-34127 Trieste Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova I-35131 Padova Italy Dipartimento di Fisica 'G. Galilei' Università di Padova I-35131 Padova Italy Dipartimento di Fisica 'M. Merlin' dell'Università e del Politecnico di Bari I-70126 Bari Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari 70126 Bari Italy Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 Palaiseau France Department of Physics University of Washington Seattle WA 98195-1560 USA Institut de Ciències de l'Espai (IEEE-CSIC) Campus UAB 08193 Barcelona Spain INAF-Istituto di Astrofisica Spaziale e Fisica Cosmica I-20133 Milano Italy Agenzia Spaziale Italiana (ASI) Science Data Center I-00044 Frascati (Roma) Italy National Research Council Research Associate National Academy of Sciences Washington DC 20001 USA ASI Science Data Center I-00044 Frascati (Roma)

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High-dimensional Gaussian graphical model selection: walk summability and local separation criterion

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2012年第1期13卷

作者： Animashree Anandkumar Vincent Y. F. Tan Furong Huang Alan S. Willsky Electrical Engineering and Computer Science University of California Irvine Irvine CA Data Mining Department Institute for Infocomm Research Singapore Electrical and Computer Engineering National University of Singapore Stochastic Systems Group Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA

We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n = Ω(Jmin-2 log p), where p is the number of variables and Jmin is the minimum (absolute) edge potential of the graphical model. The sufficient conditions for sparsistency are based on the notion of walk-summability of the model and the presence of sparse local vertex separators in the underlying graph. We also derive novel non-asymptotic necessary conditions on the number of samples required for sparsistency.

关键词： Gaussian graphical model selection high-dimensional learning local-separation property necessary conditions for model selection walk-summability

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