scholarly journals Filtration shrinkage, the structure of deflators, and failure of market completeness

2020 ◽  
Vol 24 (4) ◽  
pp. 871-901 ◽  
Author(s):  
Constantinos Kardaras ◽  
Johannes Ruf
Keyword(s):  
Local Martingale ◽  
Bayesian Filtering ◽  
Continuous Path ◽  
Information Market ◽  
Market Completeness ◽  
Filtering Approach

Abstract We analyse the structure of local martingale deflators projected on smaller filtrations. In a general continuous-path setting, we show that the local martingale parts in the multiplicative Doob–Meyer decomposition of projected local martingale deflators are themselves local martingale deflators in the smaller information market. Via use of a Bayesian filtering approach, we demonstrate the exact mechanism of how updates on the possible class of models under less information result in the strict supermartingale property of projections of such deflators. Finally, we demonstrate that these projections are unable to span all possible local martingale deflators in the smaller information market, by investigating a situation where market completeness is not retained under filtration shrinkage.

Improved Bayesian-Based Spam Filtering Approach

2013 ◽  
Vol 401-403 ◽  
pp. 1885-1891
Author(s):  
Xue Jiang ◽  
Jun Kai Yi
Keyword(s):  
Beneficial Effect ◽  
Experimental Result ◽  
Bayesian Filtering ◽  
Actual Situation ◽  
Spam Filtering ◽  
Bayesian Filter ◽  
Filtering Algorithm ◽  
Spam Filter ◽  
Filter Approach ◽  
Filtering Approach

Bayesian filtering approach is widely used in the field of anti-spam now. However, the two assumptions of this algorithm are significantly different with the actual situation so as to reduce the accuracy of the algorithm. This paper proposes a detailed improvement on researching of Bayesian Filtering Algorithm principle and implement method. It changes the priori probability of spam from constant figure to the actual probability, improves selection and selection rules of the token, and also adds URL and pictures to the detection content. Finally it designs a spam filter based on improved Bayesian filter approach. The experimental result of this improved Bayesian Filter approach indicates that it has a beneficial effect in the spam filter application.

Considering measurement uncertainty in dynamic object tracking for autonomous driving applications

2018 ◽  
Vol 85 (12) ◽  
pp. 764-778
Author(s):  
Benjamin Naujoks ◽  
Torsten Engler ◽  
Martin Michaelis ◽  
Thorsten Luettel ◽  
Hans-Joachim Wuensche
Keyword(s):  
Measurement Uncertainty ◽  
State Estimation ◽  
Object Tracking ◽  
Real World ◽  
Main Part ◽  
Autonomous Driving ◽  
Bayesian Filtering ◽  
Dynamic Object ◽  
Measurement Models ◽  
Filtering Approach

Abstract Measurement uncertainty plays an important role in every real-world perception task. This paper describes the influence of measurement uncertainty in state estimation, which is the main part of Dynamic Object Tracking. Its base is the probabilistic Bayesian Filtering approach. Practical examples and tools for choosing the correct filter implementation including measurement models and their conversion, for different kinds of sensors are presented.

scholarly journals A Bayesian Filtering Approach for Error Mitigation in Ultra-Wideband Ranging

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 440 ◽  
Author(s):  
Jing Xin ◽  
Kaiyuan Gao ◽  
Mao Shan ◽  
Bo Yan ◽  
Ding Liu
Keyword(s):  
Real Time ◽  
Ultra Wideband ◽  
Line Of Sight ◽  
Time Of Arrival ◽  
Indoor Environments ◽  
Bayesian Filtering ◽  
Tracking Accuracy ◽  
Noise Characteristics ◽  
Error Mitigation ◽  
Filtering Approach

Ultra-wideband (UWB) sensors have been widely used in multi-robot systems for cooperative tracking and positioning purposes due to their advantages such as high ranging accuracy and good real-time performance. In order to reduce the influence of non-line-of-sight (NLOS) UWB communication caused by the presence of obstacles on ranging accuracy in indoor environments, the paper proposes a novel Bayesian filtering approach for UWB ranging error mitigation. Nonparametric UWB sensor models, namely received signal strength (RSS) model and time of arrival (TOA) model, are constructed to capture the probabilistic noise characteristics under the influence of different obstruction conditions and materials within a typical indoor environment. The proposed Bayesian filtering approach can be used either as a standalone error mitigation approach for peer-to-peer (P2P) ranging, or as a part of a higher level Bayesian state estimation framework. Experiments were conducted to validate and evaluate the proposed approach in two configurations, i.e., inter-robot ranging, and mobile robot tracking in a wireless sensor network. The experimental results show that the proposed method can accurately identify the line-of-sight (LOS) and NLOS scenarios with wood and metal obstacles in a probabilistic representation and effectively improve the ranging/tracking accuracy. In addition, the low computational overhead of the approach makes it attractive in real-time systems.

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