Robust control design is mainly devoted to guarantee closed-loop stability of a model-based control law in presence of parametric and structural uncertainties. The control law is usually a complex feedback law which is derived from a (nonlinear) model, possibly complemented with some mathematical envelope of the model uncertainty. Stability may be guarantee with the help of some ignorance coeff...

We propose a computationally-tractable optimization-based framework for risk management in midterm process planning under uncertainty. We employ stochastic programming to account for the uncertainty in which a scenario-based approach is used to represent the underlying probability distribution of the uncertain parameters. The problem is formulated as a two-stage stochastic program with recourse...

Visual scene understanding is a difficult problem, interleaving object detection, geometric reasoning and scene classification. Consider the scene in Fig. 1.(a). A scene classifier will tell you, with some uncertainty, that this is a dining room [6, 3]. A layout estimator [5, 7] will tell you, with different uncertainty, how to fit a box to the room. An object detector [8, 4] will tell you, wit...

We introduce the antiparticle filter, AF, a new type of recursive Bayesian estimator that is unlike either the extended Kalman Filter, EKF, unscented Kalman Filter, UKF or the particle filter PF. We show that for a classic problem of robot localization the AF can substantially outperform these other filters in some situations. The AF estimates the posterior distribution as an auxiliary variable...

To facilitate offline reinforcement learning, uncertainty estimation is commonly used to detect out-of-distribution data. By inspecting, we show that current explicit estimators such as Monte Carlo Dropout and model ensemble are not competent provide trustworthy in learning. Accordingly, propose a non-parametric distance-aware estimator which sensitive the change input space for Based on our ne...

This paper reports new contributions to the advancement of wind power uncertainty forecasting beyond the current state-of-the-art. A new kernel density forecast (KDF) method applied to the wind power problem is described. The method is based on the Nadaraya-Watson estimator, and a time-adaptive version of the algorithm is also proposed. Results are presented for different casestudies and compar...

The discrete Kalman lter, which is becoming a common tool for reducing uncertainty in robot navigation, suuers from some basic limitations when used for such applications. In this paper, we describe a recursive total least squares estimator (RTLS) as an alternative to the Kalman lter, and compare their performances in three sets of experiments involving problems in robot navigation. In all case...

Continued With uncertainty taken directly on the noise samples, we develop an estimation-detection theoretic approach: the detection statistic preserves the structure of the quadrature matched filter, but in place of the linear sample mean, a minimax robust estimator of the random amplitude is substituted. This test is shown to be asymptotically maximin optimal (in the sense of Huber) for a wid...

Salah satu hal penting dalam analisis statistik adalah prosedur estimasi suatu fungsi padat peluang yang biasa disebut densitas. Ada dua metode pendekatan biasanya digunakan, yaitu parameter terkait dengan asumsi distribusi tertentu dan densitas secara non parametrik. Metode parametrik sering kita jumpai histogram.
 Beberapa kelemahan histogram menjadi acuan untuk dikembangkannya lain kern...

This paper describes how to specify probability models for data analysis via a backward induction procedure. The new approach yields coherent, priorfree uncertainty assessment. The backward induction approach is first demonstrated on two familiar models — the Bernoulli distribution and the Gaussian distribution — to compare the resulting specifications to their standard counterparts arising as ...