Optimal Decision Theory Applied to High-Speed IC Receiver Design

1995 ◽  
pp. 105-161
Author(s):  
Aaron Buchwald ◽  
Kenneth W. Martin
Keyword(s):  
Decision Theory ◽  
High Speed ◽  
Optimal Decision ◽  
Receiver Design ◽  
Optimal Decision Theory

scholarly journals Research on Railway Speed-up Scheme Based on Grey Decision Theory

2020 ◽  
Author(s):  
Kexin Zhang ◽  
Jiaying Han ◽  
Xiaotie Wang
Keyword(s):  
Decision Making ◽  
Qualitative Analysis ◽  
High Speed ◽  
Optimal Decision ◽  
Grey System ◽  
High Speed Railway ◽  
Optimal Scheme ◽  
Efficiency Cost ◽  
Speed Up ◽  
Optimal Decision Making

In this paper, the decision-making problem of speed-up scheme for existing high-speed railway is studied. Considering the different running speed of different parts of high-speed railway, the speed-up scheme of existing line is formulated. Taking the factors affecting the decision-making of railway speed-up scheme into consideration, the efficiency, cost and influence are determined as the evaluation indexes, and the grey target decision-making method in grey system theory is selected to evaluate each scheme. In this problem, qualitative analysis can be used for the optimal decision-making of the initial scheme. The corresponding situations under each index are comprehensively considered and sorted. The value is assigned to each qualitative evaluation to form the effect vector of each situation. In the spherical grey target formed by grey target decision method, the optimal scheme can be obtained by comparing the off-target distance of each effect vector corresponding to the scheme. It is proved that the grey target decision-making method can also obtain the optimal scheme through qualitative analysis when it is difficult to make quantitative analysis.

scholarly journals Classification under uncertainty: data analysis for diagnostic antibody testing

2021 ◽  
Author(s):  
Paul N Patrone ◽  
Anthony J Kearsley
Keyword(s):  
Optimal Decision ◽  
Classification Error ◽  
Operating Characteristics ◽  
Antibody Testing ◽  
Probability Models ◽  
New Strategy ◽  
Serology Test ◽  
Classification Under Uncertainty ◽  
Optimal Decision Theory ◽  
Estimate Prevalence

Abstract Formulating accurate and robust classification strategies is a key challenge of developing diagnostic and antibody tests. Methods that do not explicitly account for disease prevalence and uncertainty therein can lead to significant classification errors. We present a novel method that leverages optimal decision theory to address this problem. As a preliminary step, we develop an analysis that uses an assumed prevalence and conditional probability models of diagnostic measurement outcomes to define optimal (in the sense of minimizing rates of false positives and false negatives) classification domains. Critically, we demonstrate how this strategy can be generalized to a setting in which the prevalence is unknown by either (i) defining a third class of hold-out samples that require further testing or (ii) using an adaptive algorithm to estimate prevalence prior to defining classification domains. We also provide examples for a recently published SARS-CoV-2 serology test and discuss how measurement uncertainty (e.g. associated with instrumentation) can be incorporated into the analysis. We find that our new strategy decreases classification error by up to a decade relative to more traditional methods based on confidence intervals. Moreover, it establishes a theoretical foundation for generalizing techniques such as receiver operating characteristics by connecting them to the broader field of optimization.

scholarly journals Regret and the rationality of choices

2010 ◽  
Vol 365 (1538) ◽  
pp. 249-257 ◽  
Author(s):  
Sacha Bourgeois-Gironde
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Clinical Studies ◽  
Optimal Decision ◽  
Error Signal ◽  
Predictive Error ◽  
Optimal Decision Making ◽  
Choice Regret

Regret helps to optimize decision behaviour. It can be defined as a rational emotion. Several recent neurobiological studies have confirmed the interface between emotion and cognition at which regret is located and documented its role in decision behaviour. These data give credibility to the incorporation of regret in decision theory that had been proposed by economists in the 1980s. However, finer distinctions are required in order to get a better grasp of how regret and behaviour influence each other. Regret can be defined as a predictive error signal but this signal does not necessarily transpose into a decision-weight influencing behaviour. Clinical studies on several types of patients show that the processing of an error signal and its influence on subsequent behaviour can be dissociated. We propose a general understanding of how regret and decision-making are connected in terms of regret being modulated by rational antecedents of choice. Regret and the modification of behaviour on its basis will depend on the criteria of rationality involved in decision-making. We indicate current and prospective lines of research in order to refine our views on how regret contributes to optimal decision-making.

scholarly journals Optimal Decision-Making Model of Agricultural Product Information Based on Three-Way Decision Theory

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 41
Author(s):  
Yifan Gu ◽  
Zishang Yang ◽  
Tailong Zhu ◽  
Junshu Wang ◽  
Yuxing Han
Keyword(s):  
Decision Making ◽  
Decision Theory ◽  
Product Information ◽  
Heuristic Method ◽  
Agricultural Products ◽  
Semantic Interpretation ◽  
Optimal Decision ◽  
Agricultural Product ◽  
Product Attribute ◽  
Attribute Information

As an effective heuristic method, three-way decision theory gives a new semantic interpretation to the three fields of the rough set, which has a huge application space. To classify the information of agricultural products more accurately under certain thresholds, this paper first makes a comprehensive evaluation of the decision, particularly the influence of the attributes of the event itself on the results and their interactions. By using fuzzy sets corresponding to membership and non-membership degree, this paper analyzes and puts forward two cases of proportional correlation coefficients in the transformation of a delayed decision domain, and selects the corresponding coefficients to compare the results directly. Finally, consumers can conveniently grasp product attribute information to make decisions. On this basis, this paper analyzed the standard data to verify the accuracy of the model. After that, the proposed algorithm, based on three decision-making agricultural product information classification processing, is applied to the relevant data of agricultural products. The experimental results showed that the algorithm can obtain more accurate results through a more straightforward calculation process. It can be concluded that the algorithm proposed in this paper can enable people to make more convenient and accurate decisions based on product attribute information.

scholarly journals Impulsive noise modeling and robust receiver design

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Laurent Clavier ◽  
Gareth W. Peters ◽  
François Septier ◽  
Ido Nevat
Keyword(s):  
Communication Systems ◽  
Gaussian Approximation ◽  
Impulsive Noise ◽  
Decision Rules ◽  
Significant Loss ◽  
Optimal Decision ◽  
Receiver Design ◽  
Impulsive Behavior ◽  
Linear Effect ◽  
Linear Approach

AbstractInterference is an important limitation in many communication systems. It has been shown in many situations that the popular Gaussian approximation is not adequate and interference exhibits an impulsive behavior. This paper surveys the different statistical models proposed for such an interference, that can generally be unified using the class of sub-exponential family of distributions, and its impact on the receiver design. Visualizing the optimal decision boundaries allows one to show the non linear effect induced by impulsive noise models, which explains the significant loss in receiver performance designed under the standard Gaussian approximation. This motivates the need to develop new receivers. We propose a framework to design receivers robust to a variety of interference types, both Gaussian and non-Gaussian. We explore three ways of thinking about such receiver designs: a linear approach; by approximating the noise plus interference distribution; and by mimicking the decision rule distribution directly. Except for the linear approach, the other designs are capable of replicating the non-trivial optimal decision regions to different extents. The new detection algorithms are evaluated via Monte Carlo simulations. We focus on four efficient architectures, including the parameter estimations: Myriad, Normal Inverse Gaussian, p-norm and a direct estimation of the likelihood ratio function. They exhibit good performance, close to the optimal, in a large range of situations demonstrating they may be considered as robust decision rules in the presence of heavy tailed or impulsive interference environments.

scholarly journals Machines Imitating Human Thinking Using Bayesian Learning and Bootstrap

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 389
Author(s):  
Sunghae Jun
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Decision Theory ◽  
Bayesian Learning ◽  
Bayesian Decision Theory ◽  
Optimal Decision ◽  
Bayesian Decision ◽  
Specific Domain ◽  
Human Thinking ◽  
Optimal Decision Making

In the field of cognitive science, much research has been conducted on the diverse applications of artificial intelligence (AI). One important area of study is machines imitating human thinking. Although there are various approaches to development of thinking machines, we assume that human thinking is not always optimal in this paper. Sometimes, humans are driven by emotions to make decisions that are not optimal. Recently, deep learning has been dominating most machine learning tasks in AI. In the area of optimal decisions involving AI, many traditional machine learning methods are rapidly being replaced by deep learning. Therefore, because of deep learning, we can expect the faster growth of AI technology such as AlphaGo in optimal decision-making. However, humans sometimes think and act not optimally but emotionally. In this paper, we propose a method for building thinking machines imitating humans using Bayesian decision theory and learning. Bayesian statistics involves a learning process based on prior and posterior aspects. The prior represents an initial belief in a specific domain. This is updated to posterior through the likelihood of observed data. The posterior refers to the updated belief based on observations. When the observed data are newly added, the current posterior is used as a new prior for the updated posterior. Bayesian learning such as this also provides an optimal decision; thus, this is not well-suited to the modeling of thinking machines. Therefore, we study a new Bayesian approach to developing thinking machines using Bayesian decision theory. In our research, we do not use a single optimal value expected by the posterior; instead, we generate random values from the last updated posterior to be used for thinking machines that imitate human thinking.

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