{"product_id":"9781441928870","title":"Information Science and Statistics","description":"\u003ch1\u003eInformation Science and Statistics\u003c\/h1\u003e \u003ch2\u003eDoucet, Arnaud; Smith, A.; Freitas, Nando de; Gordon, Neil\u003c\/h2\u003e \u003cp\u003eMonte Carlo methods are revolutionising the on-line analysis of data \nin fields as diverse as financial modelling, target tracking and \ncomputer vision. These methods, appearing under the names of bootstrap \nfilters, condensation, optimal Monte Carlo filters, particle filters \nand survial of the fittest, have made it possible to solve numerically \nmany complex, non-standarard problems that were previously \nintractable. \n This book presents the first comprehensive treatment of these \ntechniques, including convergence results and applications to \ntracking, guidance, automated target recognition, aircraft navigation, \nrobot navigation, econometrics, financial modelling, neural \nnetworks,optimal control, optimal filtering, communications, \nreinforcement learning, signal enhancement, model averaging and \nselection, computer vision, semiconductor design, population biology, \ndynamic Bayesian networks, and time series analysis.  This will be of \ngreat value to students, researchers and practicioners, who have some \nbasic knowledge of probability. \nArnaud Doucet received the Ph. D. degree from the University of Paris- \nXI Orsay in 1997.  From 1998 to 2000, he conducted research at the \nSignal Processing Group of Cambridge University, UK.  He is currently \nan assistant professor at the Department of Electrical Engineering of \nMelbourne University, Australia.  His research interests include \nBayesian statistics, dynamic models and Monte Carlo methods. \nNando de Freitas obtained a Ph.D. degree in information engineering \nfrom Cambridge University in 1999. He is presently a research \nassociate with the artificial intelligence group of the University of \nCalifornia at Berkeley.  His main research interests are in Bayesian \nstatistics and the application of on-line and batch Monte Carlo \nmethods to machine learning.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Springer\u003c\/p\u003e \u003cp\u003ePublication Date: 2010-12-01\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9781441928870\u003c\/p\u003e \u003cp\u003eDOI: 10.1007\/978-1-4757-3437-9\u003c\/p\u003e \u003cp\u003eDimensions: 235cm x155cm\u003c\/p\u003e \u003cp\u003ePages: 582\u003c\/p\u003e ","brand":"Springer New York","offers":[{"title":"Default Title","offer_id":45987526181004,"sku":"9781441928870","price":296.1,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9781441928870.jpg?v=1779042872","url":"https:\/\/lateknightbooks.com\/products\/9781441928870","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}