A Crash Prediction Algorithm Using a Particle Filter and Bayesian Decision Theory

2009 ◽  
Author(s):  
Taewung Kim ◽  
Hyun-Yong Jeong
Keyword(s):  
Decision Theory ◽  
Particle Filter ◽  
Particle Filtering ◽  
Threat Assessment ◽  
Prediction Algorithm ◽  
Optimal Decision ◽  
Statistical Decision Theory ◽  
Bayesian Decision ◽  
Statistical Decision ◽  
Crash Prediction

Active safety systems have been developed in automotive industry, and a tracking algorithm and a threat assessment algorithm are needed in such systems to predict the collision between vehicles. It is difficult to track a threat vehicle accurately because of lack of information on a threat vehicle and the measurement noise which does normally not follow Gaussian distribution. Therefore, there is an uncertainty whether the collision will occur or not. Particle filtering is widely used for nonlinear and non-Gaussian tracking problems, and statistical decision theory can be used to make an optimal decision in an uncertain case. In this study, a crash prediction algorithm has been developed using a particle filter and statistical decision making.

scholarly journals The Bayesian Decision Model with more than One Predictor—An Application to the Stocking Rate Problem

1971 ◽  
Vol 3 (1) ◽  
pp. 95-102 ◽  
Author(s):  
Freddie C. White ◽  
Vernon R. Eidman
Keyword(s):  
Decision Theory ◽  
Bayesian Analysis ◽  
Decision Model ◽  
Decision Problems ◽  
Statistical Decision Theory ◽  
Stocking Rate ◽  
Bayesian Decision ◽  
Statistical Decision ◽  
Empirical Results ◽  
Recent Developments

The variables a manager faces in making decisions may be divided into two broad categories—those which are determined by the manager and those which are outside of his control. Agricultural economists have made many efforts to develop expectation models for one or more of the uncontrollable variables facing farmers and have suggested procedures for utilizing the resulting expectations. Recent developments in statistical decision theory provide a logically consistent framework for incorporating the predictions of expectation models [4, pp. 192-196]. Applications of Bayesian analysis utilizing predictions of one uncontrollable variable have been reported in the literature [1, 3]. However, many decision problems logically require expectations of two uncontrollable variables (such as price and yield) or more. This article illustrates a method of including predictors for more than one uncontrollable variable in the Bayesian framework, and reports some empirical results of an application to a stocking rate problem.

Representation, abstraction, and simple-minded sophisticates

2020 ◽  
Vol 43 ◽  
Author(s):  
Peter Dayan
Keyword(s):  
Decision Theory ◽  
Predictive Coding ◽  
Bayesian Decision Theory ◽  
Bayesian Decision ◽  
Model Based ◽  
Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

scholarly journals Bayesian natural selection and the evolution of perceptual systems

2002 ◽  
Vol 357 (1420) ◽  
pp. 419-448 ◽  
Author(s):  
Wilson S. Geisler ◽  
Randy L. Diehl
Keyword(s):  
Natural Selection ◽  
Decision Theory ◽  
Ideal Observer ◽  
Statistical Decision Theory ◽  
Statistical Decision ◽  
Natural Stimuli ◽  
Ideal Observers ◽  
Formal Framework ◽  
Bayesian Statistical Decision Theory ◽  
Perceptual Systems

In recent years, there has been much interest in characterizing statistical properties of natural stimuli in order to better understand the design of perceptual systems. A fruitful approach has been to compare the processing of natural stimuli in real perceptual systems with that of ideal observers derived within the framework of Bayesian statistical decision theory. While this form of optimization theory has provided a deeper understanding of the information contained in natural stimuli as well as of the computational principles employed in perceptual systems, it does not directly consider the process of natural selection, which is ultimately responsible for design. Here we propose a formal framework for analysing how the statistics of natural stimuli and the process of natural selection interact to determine the design of perceptual systems. The framework consists of two complementary components. The first is a maximum fitness ideal observer, a standard Bayesian ideal observer with a utility function appropriate for natural selection. The second component is a formal version of natural selection based upon Bayesian statistical decision theory. Maximum fitness ideal observers and Bayesian natural selection are demonstrated in several examples. We suggest that the Bayesian approach is appropriate not only for the study of perceptual systems but also for the study of many other systems in biology.

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