# ITE / code / IPA / demos / demo_mAR_IPA.m

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 `%function [] = demo_mAR_IPA() %mAR-IPA (AutoRegressive Independent Process Analysis with missing observations) illustration. % %Model (mAR-IPA): % F[z]s = e, F[z]=I-F_1*z^1-...-F_L*z^L, F[z]:stable, e:ISA source (see 'demo_ISA.m'), % x = As, A:invertible, % y = M(x): observation. %Task: y -> A (or W=A^{-1}),s,F[z],e. % %Copyright (C) 2012 Zoltan Szabo ("http://nipg.inf.elte.hu/szzoli", "szzoli (at) cs (dot) elte (dot) hu") % %This file is part of the ITE (Information Theoretical Estimators) Matlab/Octave toolbox. % %ITE is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by %the Free Software Foundation, either version 3 of the License, or (at your option) any later version. % %This software is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of %MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. % %You should have received a copy of the GNU General Public License along with ITE. If not, see . %clear start: clear all; close all; %parameters: %dataset: %driving noise(e): data_type = 'Aw';%see 'sample_subspaces.m' num_of_comps = 3;%number of components/subspaces in sampling %hidden source(s): num_of_samples = 5*1000;%number of samples L = 1; %AR order F_lambda = 0.7; %AR stability parameter, 0