Advances in Intelligent Data Analysis XIII: 13th by Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti PDF

By Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti

ISBN-10: 3319125702

ISBN-13: 9783319125701

ISBN-10: 3319125710

ISBN-13: 9783319125718

This booklet constitutes the refereed convention court cases of the thirteenth foreign convention on clever info research, which was once held in October/November 2014 in Leuven, Belgium. The 33 revised complete papers including three invited papers have been rigorously reviewed and chosen from 70 submissions dealing with every kind of modeling and research tools, without reference to self-discipline. The papers hide all features of clever information research, together with papers on clever help for modeling and studying info from complicated, dynamical systems.

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Additional resources for Advances in Intelligent Data Analysis XIII: 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 – November 1, 2014. Proceedings

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We provide an alternative constructive method to obtain continuous–time AR processes (CAR), a particular case of the CARMA models, from repeated applications of an integral operator (the OU operator). This construction can be extended to apply to a moving average of the noise as well, and hence provides a different approach to the CARMA models, that can lead to further generalizations. Regarding the estimation of our continuous AR model (the OU(p) process) from discretely observed data, we use the state–space representation method as in [5], and derive analogous results on their representability as discrete ARMA(p, p − 1) processes.

2 of [7]). The transitions in the state space are ξ κ (t) = diag(e−κ1 , . . , e−κp )ξ κ (t − 1) + ηκ (t), and x(t) = K tr (κ)ξ(t) The innovations have variance matrix Var(η κ,τ (t)) = ((vj,l )), where vj,l = −(κj +¯ κl ) t σ E t−1 e−(κj +¯κl )(t−s) ds = 1−eκj +¯κl . p Now apply the AR operator j=1 (1 − e−κj B) to xκ and obtain 2 p p (1 − e−κj B)xκ (t) = σ j=1 with Gj (z) = l=j (1 Kj Gj (B)ηκj (t) =: ζ(t), j=1 − e−κj z) := 1 − p−1 l=1 gj,l z l . 18 A. Arratia, A. M. Caba˜ na This process has the same second-order moments as the ARMA(p, p − 1), p−1 − e−κj B)xκ (t) = j=0 θj (t − j) =: ζ (t) ( is a white noise), when ¯ − j) and c = Eζ (t)ζ¯ (t − j) coincide.

1 Introduction Large software systems tend to have complex structures that are often difficult to comprehend due to the large number of modules and inter-relationships that exist between them. As the modular structure of a software system tends to decay over time, it is important to modularise. Modularisation can facilitate program understanding and makes the problem at hand easier to understand, as it reduces the amount of data needed by developers [7]. Modularisation is the process of partitioning the structure of software system into subsystems.

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Advances in Intelligent Data Analysis XIII: 13th International Symposium, IDA 2014, Leuven, Belgium, October 30 – November 1, 2014. Proceedings by Hendrik Blockeel, Matthijs van Leeuwen, Veronica Vinciotti


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