Many important functions over strings can be represented as finite-state string transducers. In this paper, we present an automatatheoretic technique for algorithmically verifying that such a function is robust to uncertainty. A function encoded as a transducer is defined to be robust if for each small (i.e., bounded) change to any input string, the change in the transducer’s output is proporti...

SUMMARY This paper exposits and develops Bayesian methods of model criticism and robustness analysis. The objectives are to clarify the Bayesian interpretation of non-Bayesian diagnostic tests, and provide explicitly Bayesian procedures accessible to practical investigators. Speciic methods for prior density criticism and robustness analysis, and data density criticism, are presented. All are b...

ECONOMICS AS ROBUSTNESS ANALYSIS Jaakko Kuorikoski, Aki Lehtinen and Caterina Marchionni 25.9. 2007

Synchrosqueezed transforms are non-linear processes for a sharpened time-frequency representation of wave-like components. They are efficient tools for identifying and analyzing wave-like components from their superposition. This paper is concerned with the statistical properties of compactly supported synchrosqueezed transforms for wave-like components embedded in a generalized Gaussian random...

despite providing robustness, high-gain observers impose a peaking phenomenon, which may cause instability, on the system states. in this paper, an adaptive saturation is proposed to attenuate the undesirable mentioned phenomenon in high-gain observers. a real-valued and differentiable sigmoid function is considered as the saturating element whose parameters (height and slope) are adaptively tu...

The paper develops a new approach for robot self-localization in the Robocup Midsize league. The approach is based on modeling the quality of an estimate using an error term and numerically minimizing it. Furthermore, we derive the reliability of the estimate analyzing the error function and apply the derived uncertainty value to a sensor integration process. The approach is characterized by hi...

Probabilistic calibration is the task of producing reliable estimates of the conditional class probability P (class|observation) from the outputs of numerical classifiers. A recent comparative study [1] revealed that Isotonic Regression [2] and Platt Calibration [3] are most effective probabilistic calibration technique for a wide range of classifiers. This paper will demonstrate that these met...