Journal ArticleDOI
Theory and practice of recursive identification
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This article is published in IEEE Transactions on Automatic Control.The article was published on 1985-10-01. It has received 940 citations till now. The article focuses on the topics: Recursive Bayesian estimation & Identification (information).read more
Citations
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Alignment by Maximization of Mutual Information
Paul A. Viola,William M. Wells +1 more
TL;DR: A new information-theoretic approach is presented for finding the pose of an object in an image that works well in domains where edge or gradient-magnitude based methods have difficulty, yet it is more robust than traditional correlation.
Dynamic bayesian networks: representation, inference and learning
Kevin Murphy,Stuart Russell +1 more
TL;DR: This thesis will discuss how to represent many different kinds of models as DBNs, how to perform exact and approximate inference in Dbns, and how to learn DBN models from sequential data.
Journal ArticleDOI
Hierarchical mixtures of experts and the EM algorithm
TL;DR: An Expectation-Maximization (EM) algorithm for adjusting the parameters of the tree-structured architecture for supervised learning and an on-line learning algorithm in which the parameters are updated incrementally.
Journal ArticleDOI
Multi-modal volume registration by maximization of mutual information
William M. Wells,William M. Wells,Paul A. Viola,Paul A. Viola,Hideki Atsumi,Shin Nakajima,Ron Kikinis +6 more
TL;DR: In this paper, an information-theoretic approach for finding the registration of volumetric medical images of differing modalities is presented, which is achieved by adjustment of the relative position and orientation until the mutual information between the images is maximized.
Book
Kalman Filtering and Neural Networks
TL;DR: This book takes a nontraditional nonlinear approach and reflects the fact that most practical applications are nonlinear.
References
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Journal ArticleDOI
Discrete Time Stochastic Adaptive Control
TL;DR: It is shown that, with probability one, the algorithm will ensure the system inputs and outputs are sample mean square bounded and the conditional mean square output tracking error achieves its global minimum possible value for linear feedback control.
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Applications of a Kushner and Clark lemma to general classes of stochastic algorithms
M. Metivier,P. Priouret +1 more
TL;DR: It is shown how one can apply a lemma of Kushner and Clark to obtain properties of stochastic algorithms, including algorithms considered by Ljung as well as algorithms of the form \theta_{n+1} = \theTA_{n} - \gamma_{n-1} V_{n +1}(\theta_n, Z) , where Z is a stationary ergodic process.
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Adaptive control with the stochastic approximation algorithm: Geometry and convergence
TL;DR: In this article, it was shown that if the system does not have a reduced-order minimum variance controller, then the parameter estimates converge to a random multiple of the true parameter.
Journal ArticleDOI
An Invariant Measure Approach to the Convergence of Stochastic Approximations with State Dependent Noise.
Harold J. Kushner,Adam Shwartz +1 more
TL;DR: In this article, a new method for quickly getting the ODE associated with the asymptotic properties of the stochastic approximation of the projected algorithm for the constrained problem is presented.