03354nam a22004935i 4500001001800000003000900018005001700027007001500044008004100059020001800100020001900118024003500137040000900172082001400181100002500195245009500220264006700315300003500382336002600417337002600443338003900469347002400508490006700532505044000599520116101039650001702200650001302217650002402230650003902254650001702293650004902310650003702359650002402396650002602420700002602446710003402472773002002506776003602526830006702562856010302629942001202732999001702744952009902761978-0-387-76896-0DE-He21320260521091958.0cr nn 008mamaa130821s2009    xxu|    s    |||| 0|eng d  a9780387768960  a997803877689607 a10.1007/978-0-387-76896-02doi  cCICY04a519.22231 aBain, Alan.eauthor.10aFundamentals of Stochastic Filteringh[recurso electrónico] /cby Alan Bain, Dan Crisan. 1aNew York, NY :bSpringer New York :bImprint: Springer,c2009.  aXIII, 390 p.bonline resource.  atextbtxt2rdacontent  acomputerbc2rdamedia  arecurso en líneabcr2rdacarrier  atext filebPDF2rda1 aStochastic Modelling and Applied Probability,x0172-4568 ;v600 aFiltering Theory -- The Stochastic Process ? -- The Filtering Equations -- Uniqueness of the Solution to the Zakai and the Kushner-Stratonovich Equations -- The Robust Representation Formula -- Finite-Dimensional Filters -- The Density of the Conditional Distribution of the Signal -- Numerical Algorithms -- Numerical Methods for Solving the Filtering Problem -- A Continuous Time Particle Filter -- Particle Filters in Discrete Time.  aThe objective of stochastic filtering is to determine the best estimate for the state of a stochastic dynamical system from partial observations. The solution of this problem in the linear case is the well known Kalman-Bucy filter which has found widespread practical application. The purpose of this book is to provide a rigorous mathematical treatment of the non-linear stochastic filtering problem using modern methods. Particular emphasis is placed on the theoretical analysis of numerical methods for the solution of the filtering problem via particle methods. The book should provide sufficient background to enable study of the recent literature. While no prior knowledge of stochastic filtering is required, readers are assumed to be familiar with measure theory, probability theory and the basics of stochastic processes. Most of the technical results that are required are stated and proved in the appendices. The book is intended as a reference for graduate students and researchers interested in the field. It is also suitable for use as a text for a graduate level course on stochastic filtering. Suitable exercises and solutions are included. 0aMATHEMATICS. 0aFINANCE. 0aNUMERICAL ANALYSIS. 0aDISTRIBUTION (PROBABILITY THEORY).14aMATHEMATICS.24aPROBABILITY THEORY AND STOCHASTIC PROCESSES.24aCONTROL, ROBOTICS, MECHATRONICS.24aNUMERICAL ANALYSIS.24aQUANTITATIVE FINANCE.1 aCrisan, Dan.eauthor.2 aSpringerLink (Online service)0 tSpringer eBooks08iPrinted edition:z9780387768953 0aStochastic Modelling and Applied Probability,x0172-4568 ;v6040uhttp://dx.doi.org/10.1007/978-0-387-76896-0zVer el texto completo en las instalaciones del CICY  2ddccER  c34781d34781  00102ddc40708LEaCICYbCICYcELd2025-07-10l0o519.2r2025-07-10 08:40:25w2025-07-10yER