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Completing the State Space with Subjective States
Journal of Economic Theory, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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2003 International Conference on Multimedia and Expo. ICME '03. Proceedings (Cat. No.03TH8698), 2003
The Kalman filter is the linear optimal estimator for random signals. We develop state-space RLS that is the counterpart of the Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares.
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The Kalman filter is the linear optimal estimator for random signals. We develop state-space RLS that is the counterpart of the Kalman filter for deterministic signals i.e. there is no process noise but only observation noise. State-space RLS inherits its optimality properties from the standard least squares.
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SIAM Review, 1973
Summary: A resolution space, recently introduced for the study of causality in an operator theoretic setting, is employed to formulate an abstract state concept which generalizes the state space theory commonly used in the study of finite-dimensional dynamical systems.
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Summary: A resolution space, recently introduced for the study of causality in an operator theoretic setting, is employed to formulate an abstract state concept which generalizes the state space theory commonly used in the study of finite-dimensional dynamical systems.
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The statistics of state-spaces
Annals of Mathematics and Artificial Intelligence, 1990The state-space model is a general, powerful, and elegant representation of problem solving. Nevertheless, state-spaces have rarely been used to model realistic environments because conventional state-spaces are inherently deterministic, while the world is not.
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THE LOGIC OF INFORMATION IN STATE SPACES
The Review of Symbolic Logic, 2020AbstractState spaces are, in the most general sense, sets of entities that contain information. Examples include states of dynamical systems, processes of observations, or possible worlds. We use domain theory to describe the structure of positive and negative information in state spaces.
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2010
A very general model that subsumes a whole class of special cases of interest in much the same way that linear regression does is the state-space model or the dynamic linear model, which was introduced in Kalman [112] and Kalman and Bucy [113]. The model arose in the space tracking setting, where the state equation defines the motion equations for the ...
Robert H. Shumway, David S. Stoffer
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A very general model that subsumes a whole class of special cases of interest in much the same way that linear regression does is the state-space model or the dynamic linear model, which was introduced in Kalman [112] and Kalman and Bucy [113]. The model arose in the space tracking setting, where the state equation defines the motion equations for the ...
Robert H. Shumway, David S. Stoffer
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2008
State space models is a rather loose term given to time series models, usually formulated in terms of unobserved components, that make use of the state space form for their statistical treatment.
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State space models is a rather loose term given to time series models, usually formulated in terms of unobserved components, that make use of the state space form for their statistical treatment.
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Space for capital, space for states
Theory and Society, 1986If you open your nearest historical atlas to the political map of Europe around 1500, several features will strike your eye at once. First, the large monochromas that represent more or less continuous sovereignty over extensive territories spiral around the map's periphery: the Ottoman Empire, the Spanish domains in the Iberian peninsula and the ...
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1997
The methods based on transfer functions are often referred to as Classical Methods; they are quite sufficient for the design of single-input single-output (SISO) control systems, but they become difficult to apply to multi-input multi-output (MIMO) systems.
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The methods based on transfer functions are often referred to as Classical Methods; they are quite sufficient for the design of single-input single-output (SISO) control systems, but they become difficult to apply to multi-input multi-output (MIMO) systems.
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State Spaces of Convolutional Codes
Designs, Codes and Cryptography, 1997There are various definitions of convolutional codes and each definition leads to a definition of code state space. In the usual definition of a convolutional code generated by a rational encoding matrix the input sequences can be any Laurent series. It is proved that restricting input sequences to be rational functions or restricting output sequences ...
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