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Roebroeck Seth Valdes-SosaM. Havlicek, J. Jan, M. Brazdil, and V. D. Calhoun. Dynamic granger causality basedon kalman filter for evaluation of functional network connectivity in fmri data. Neuroimage,53(1):65–77, 2010.R. M. Heidemann, N. Seiberlich, M. A. Griswold, K. Wohlfarth, G. Krueger, and P. M.Jakob. Perspectives and limitations of parallel mr imaging at high field strengths.Neuroimaging Clin N Am, 16(2):311–20, 2006.R. N. Henson, C. J. Price, M. D. Rugg, R. Turner, and K. J. Friston. Detecting latencydifferences in event-related bold responses: application to words versus nonwordsand initial versus repeated face presentations. Neuroimage, 15(1):83–97, 2002.J. L. Hernandez, P. A. Valdés, and P. Vila. Eeg spike and wave modelled by a stochasticlimit cycle. NeuroReport, 1996.B. Horwitz, K. J. Friston, and J. G. Taylor. Neural modeling and functional brainimaging: an overview. Neural Netw, 13(8-9):829–46, 2000.H. Johansen-Berg and T.E.J Behrens, editors. Diffusion MRI: From quantitative measurementto in-vivo neuroanatomy. Academic Press, London, 2009.D.K. Jones, editor. Diffusion MRI: Theory, Methods, and Applications. Oxford UniversityPress, Oxford, 2010.S. J. Kiebel, M. I. Garrido, R. Moran, C. C. Chen, and K. J. Friston. Dynamic causalmodeling for eeg and meg. Hum Brain Mapp, 30(6):1866–76, 2009.C. H. Liao, K. J. Worsley, J. B. Poline, J. A. Aston, G. H. Duncan, and A. C. Evans.Estimating the delay of the fmri response. Neuroimage, 16(3 Pt 1):593–606, 2002.L. Ljung. System Identification: Theory for the User. Prentice-Hall, New Jersey, 2ndedition, 1999.N. K. Logothetis. What we can do and what we cannot do with fmri. Nature, 453(7197):869–78, 2008.N. K. Logothetis, J. Pauls, M. Augath, T. Trinath, and A. Oeltermann. Neurophysiologicalinvestigation of the basis of the fmri signal. Nature, 412(6843):150–7, 2001.H. Markram. The blue brain project. Nat Rev Neurosci, 7(2):153–60, 2006.A. C. Marreiros, S. J. Kiebel, and K. J. Friston. Dynamic causal modelling for fmri: atwo-state model. Neuroimage, 39(1):269–78, 2008.J. Roderick McCrorie. The likelihood of the parameters of a continuous time vector autoregressivemodel. Statistical Inference for Stochastic Processes, 5:273–286, 2002.J. Roderick Mccrorie. The problem of aliasing in identifying finite parameter continuoustime stochastic models. Acta Applicandae Mathematicae, 79:9–16, 2003.100
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Causality in Time SeriesVolume 5:Ca
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PrefaceThe following is a print ver
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JMLR: Workshop and Conference Proce
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Linking Granger Causality and the P
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Popescu1. IntroductionCausality is
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Popescugeneral terms, if we are pre
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PopescuType III error prob. γ = P
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Popescuwhich would correspond to a
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Popescuvalued vector. A Data Genera
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Popescuy i =K∑︁A k y i−k + Bu
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Popescuy i =K∑︁A k y i−k + Bu
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PopescuFigure 1: SVAR causality and
Page 58 and 59: Popescu6. Spectral methods and phas
Page 60 and 61: PopescuH GCj→i|u = logD i − log
Page 62 and 63: Popescu8.1. The cardinal transform
Page 64 and 65: Popescuy N,i = Bx N,i (37)y = (1
Page 66 and 67: Popescu(a) Unmixed colored noise(b)
Page 68 and 69: PopescuAs we can see in both Figure
Page 70 and 71: Popescuβ > 0 y C,i =x N,i =⎡K∑
Page 72 and 73: Popescupretation of information flo
Page 74 and 75: Popescuit is suggested that a princ
Page 76 and 77: PopescuA. N. Kolmogorov and A. N. S
Page 78 and 79: PopescuH. White and X. Lu. Granger
Page 80 and 81: PopescuTable 6: α vs. ΨN * → 50
Page 82 and 83: Roebroeck Seth Valdes-Sosaincreasin
Page 84 and 85: Roebroeck Seth Valdes-SosaFigure 1:
Page 86 and 87: Roebroeck Seth Valdes-Sosasignal fr
Page 88 and 89: Roebroeck Seth Valdes-Sosablood flo
Page 90 and 91: Roebroeck Seth Valdes-Sosaat lower
Page 92 and 93: Roebroeck Seth Valdes-SosaTable 1:
Page 94 and 95: Roebroeck Seth Valdes-Sosa1984) and
Page 96 and 97: Roebroeck Seth Valdes-Sosaanalysis
Page 98 and 99: Roebroeck Seth Valdes-Sosamarginal
Page 100 and 101: Roebroeck Seth Valdes-SosaThe initi
Page 102 and 103: Roebroeck Seth Valdes-Sosaobserved
Page 104 and 105: Roebroeck Seth Valdes-Sosaand corti
Page 106 and 107: Roebroeck Seth Valdes-SosaDaniel Co
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Page 112 and 113: Roebroeck Seth Valdes-SosaV. Walsh
Page 114 and 115: Moneta Chlass Entner HoyerA traditi
Page 116 and 117: Moneta Chlass Entner Hoyerto the
Page 118 and 119: Moneta Chlass Entner Hoyersecond st
Page 120 and 121: Moneta Chlass Entner HoyerThe Wald
Page 122 and 123: Moneta Chlass Entner HoyerStep 3 Ap
Page 124 and 125: Moneta Chlass Entner Hoyerthe struc
Page 126 and 127: Moneta Chlass Entner Hoyerstricted
Page 128 and 129: Moneta Chlass Entner Hoyerwhere h 1
Page 130 and 131: Moneta Chlass Entner Hoyerlatter re
Page 132 and 133: Moneta Chlass Entner Hoyercausal mo
Page 134 and 135: Moneta Chlass Entner HoyerL. Baring
Page 136 and 137: Moneta Chlass Entner HoyerS. Johans
Page 138 and 139: Moneta Chlass Entner HoyerA. Yatche
Page 140 and 141: Guyon Statnikov Aliferisis extremel
Page 142 and 143: Guyon Statnikov Aliferis4. A Data R
Page 144 and 145: Guyon Statnikov Aliferismethods on
Page 146 and 147: Guyon Statnikov Aliferisvolume 3, p
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