Description: In these demos, we demonstrate the use of the extended Kalman filter (EKF), unscented Kalman filter (UKF), standard particle filter (a.k.a. condensation, survival of the fittest, bootstrap filter, SIR, sequential Monte Carlo, etc.), particle filter with MCMC steps, particle filter with EKF proposal and unscented particle filter (particle filter with UKF proposal) on a simple state estimation problem and on a financial time series forecasting problem. The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eric Wan. The Unscented Particle Filter. Technical report CUED/F-INFENG/TR 380, Cambridge University Department of Engineering, May 2000. After downloading the file, type "tar-xf upf_demos.tar" to uncompress it. This creates the directory webalgorithm containing the required m files. Go to this di
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upf_demos
.........\blackscholes.m
.........\bsffun.m
.........\bshfun.m
.........\data
.........\....\c2925.prn
.........\....\c3025.prn
.........\....\c3125.prn
.........\....\c3225.prn
.........\....\c3325.prn
.........\....\p2925.prn
.........\....\p3025.prn
.........\....\p3125.prn
.........\....\p3225.prn
.........\....\p3325.prn
.........\demo_MC.m
.........\ffun.m
.........\gengamma.m
.........\hfun.m
.........\multinomialR.m
.........\residualR.m
.........\systematicR.m
.........\ukf
.........\...\scaledSymmetricSigmaPoints.m
.........\...\ukf.m
.........\ukf_bsffun.m
.........\ukf_bshfun.m
.........\ukf_ffun.m
.........\ukf_hfun.m
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