AbstractThe modeling of physical systems inherently involves constructing a mathematical approximation from observable data and/or a priori assumptions. This study refines some recent work on causal interpolation and causal approximation as system modeling techniques. Sufficient conditions for causal interpolators to approximate continuous causal systems are established. State realizations for minimal norm causal interpolators are also established
The technique of causal ordering is used to study causal and probabilistic aspects implied by model ...
In this book, we study theoretical and practical aspects of computing methods for mathematical model...
An important problem in many domains is to predict how a system will respond to interventions. This ...
AbstractThe modeling of physical systems inherently involves constructing a mathematical approximati...
Given a finite set {(xi, yi)} of ordered pairs from X × Y where X, Y are Hilbert spaces over the sam...
©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish thi...
© Swets & ZeitlingerWe propose a new method for the optimal causal representation of nonlinear syste...
Given a finite set {(xi, yi)} of ordered pairs from X x Y where X, Y are Hilbert spaces over the s...
To simulate a non-linear system on a digital computer the non-linear mapping from the space of the i...
cote interne IRCAM: Helie06fNone / NoneNational audienceLinear systems with irrational transfer func...
Infectious diseases are notorious for their complex dynamics, which make it difficult to fit models ...
The discovery of causal relationships between a set of observed variables is a fundamental problem i...
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...
<div><p>Infectious diseases are notorious for their complex dynamics, which make it difficult to fit...
First published in Proceedings of the American Mathematical Society in volume 132, number 2, by the ...
The technique of causal ordering is used to study causal and probabilistic aspects implied by model ...
In this book, we study theoretical and practical aspects of computing methods for mathematical model...
An important problem in many domains is to predict how a system will respond to interventions. This ...
AbstractThe modeling of physical systems inherently involves constructing a mathematical approximati...
Given a finite set {(xi, yi)} of ordered pairs from X × Y where X, Y are Hilbert spaces over the sam...
©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish thi...
© Swets & ZeitlingerWe propose a new method for the optimal causal representation of nonlinear syste...
Given a finite set {(xi, yi)} of ordered pairs from X x Y where X, Y are Hilbert spaces over the s...
To simulate a non-linear system on a digital computer the non-linear mapping from the space of the i...
cote interne IRCAM: Helie06fNone / NoneNational audienceLinear systems with irrational transfer func...
Infectious diseases are notorious for their complex dynamics, which make it difficult to fit models ...
The discovery of causal relationships between a set of observed variables is a fundamental problem i...
[Introduction] 'Causal modelling' is a general term that applies to a wide variety of formal method...
<div><p>Infectious diseases are notorious for their complex dynamics, which make it difficult to fit...
First published in Proceedings of the American Mathematical Society in volume 132, number 2, by the ...
The technique of causal ordering is used to study causal and probabilistic aspects implied by model ...
In this book, we study theoretical and practical aspects of computing methods for mathematical model...
An important problem in many domains is to predict how a system will respond to interventions. This ...