learning controllearning algorithmsparameter optimizationbayesian optimizationlinear fltersnon-parametric regressionnon-causal flteringModels of dynamic systems often contain uncertain parameters or do not describe all dynamics. In some cases, they cannot represent the plants accurately enough, which limits the...
estimate of the target locations from the CV model usually appears much smoother compared to the original noisy location measurements due to the constant-velocity assumption in the dynamic model. Therefore, this model has been successfully used in causal smoothing of time series such as the camera ...
A Bayesian Rule for Adaptive Control based on Causal Interventions A natural measure of adaptation can be obtained by the Kullback-Leibler (KL) divergence between the I/O distribution of the true world and the I/O ... PA Ortega,DA Braun - arXiv 被引量: 30发表: 2009年 Kalman-Filter Base...
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This reparameterization is accomplished via a bijection from the complicated causal-invertible parameter space to Euclidean space. The bijection facilitates computation of maximum likelihood estimators (MLE) via unconstrained optimization, as well as computation of Bayesian estimates via prior specification on...
The network is based on the authors'' earlier work (Ransing et al . 1995) on representing the causal relationship in the defect-metacause-rootcause form. Although the algorithm is based on the Bayesian analysis, many of the laws of probability have been altered to suit the complexities ...