Random-Walk and Accumulator Models of Psychophysical Discrimination: A Critical Evaluation

Perception ◽  
1984 ◽  
Vol 13 (1) ◽  
pp. 57-65 ◽  
Author(s):  
Richard A Heath
Keyword(s):  
Computer Simulation ◽  
Random Walk ◽  
Critical Evaluation ◽  
Line Length ◽  
Random Walk Model ◽  
Mean Response Time ◽  
Accumulator Model ◽  
The Relationship ◽  
Parameter Estimation Techniques ◽  
Accumulator Models

The accumulator model proposed by Vickers and the modified random-walk model proposed by Link and Heath are compared in their ability to account for confidence judgments in line-length discrimination tasks. The random-walk model proves to be a viable alternative to the accumulator model, and is able to account for the relationship between mean response time and confidence. The parameter estimation techniques available for the random-walk model are considered advantageous when compared with the accumulator model, because the predictions from the latter have been obtained with the use of computer simulation.

Computer Simulation of the Inclusion Removal Behavior in the Tundish and Model Evaluation

2014 ◽  
Vol 513-517 ◽  
pp. 4314-4318
Author(s):  
Yun Wang
Keyword(s):  
Computer Simulation ◽  
Random Walk ◽  
Particle Tracking ◽  
Random Walk Model ◽  
Motion Simulation ◽  
Lagrangian Particle Tracking ◽  
Lagrangian Particle ◽  
Inclusion Removal ◽  
Particle Tracking Model ◽  
Tracking Model

Computer simulation was applied to study the inclusion behavior in the tundish with Lagrangian particle tracking model including both the deterministic and random walk model. Comparing with the experiment result, the prediction result revealed that random walk model obtained more accuracy than the deterministic walk model by considering the effect of turbulence fluctuation on the inclusion movement. The accuracy of inclusion motion simulation was determined by the turbulence model and boundary condition.

Availability analysis of periodically inspected systems with random walk model

2001 ◽  
Vol 38 (4) ◽  
pp. 860-871 ◽  
Author(s):  
Lirong Cui ◽  
M. Xie
Keyword(s):  
Random Walk ◽  
Random Walk Model ◽  
Two Dimensional ◽  
Availability Analysis ◽  
Periodic Inspection ◽  
Dimensional Plane ◽  
Inspection And Maintenance ◽  
The Relationship

In this paper, the instantaneous availability of a system maintained under periodic inspection is investigated using random walk models. Two cases are considered. In the first model, the system is repaired or modified and it is assumed to be as good as new upon periodic inspection and maintenance. In the second model, the system is not modified after the inspection if the system is still working, and the condition of the system is assumed to be the same as that before the inspection. For both models the failures only can be found through the inspection. Perfect repair or replacement of a failed system is assumed to be carried out, but the time it takes can be constant or of a random length. The relationship between this problem and the random walk model in a two-dimensional plane is described. Several new results are also shown.

Agglomeration kinetics of monodisperse systems — computer simulation of agglomeration by a two-dimensional random walk model

1984 ◽  
Vol 38 (3) ◽  
pp. 211-216 ◽  
Author(s):  
H. Sunada ◽  
A. Otsuka ◽  
Y. Yamada ◽  
Y. Kawashima ◽  
H. Takernaka ◽  
...  
Keyword(s):  
Computer Simulation ◽  
Random Walk ◽  
Random Walk Model ◽  
Two Dimensional ◽  
Kinetics Of

Availability analysis of periodically inspected systems with random walk model

2001 ◽  
Vol 38 (04) ◽  
pp. 860-871 ◽  
Author(s):  
Lirong Cui ◽  
M. Xie
Keyword(s):  
Random Walk ◽  
Random Walk Model ◽  
Two Dimensional ◽  
Availability Analysis ◽  
Periodic Inspection ◽  
Dimensional Plane ◽  
Inspection And Maintenance ◽  
The Relationship

In this paper, the instantaneous availability of a system maintained under periodic inspection is investigated using random walk models. Two cases are considered. In the first model, the system is repaired or modified and it is assumed to be as good as new upon periodic inspection and maintenance. In the second model, the system is not modified after the inspection if the system is still working, and the condition of the system is assumed to be the same as that before the inspection. For both models the failures only can be found through the inspection. Perfect repair or replacement of a failed system is assumed to be carried out, but the time it takes can be constant or of a random length. The relationship between this problem and the random walk model in a two-dimensional plane is described. Several new results are also shown.

A Multi-Label Classification Algorithm Based on Random Walk Model

2010 ◽  
Vol 33 (8) ◽  
pp. 1418-1426 ◽  
Author(s):  
Wei ZHENG ◽  
Chao-Kun WANG ◽  
Zhang LIU ◽  
Jian-Min WANG
Keyword(s):  
Random Walk ◽  
Classification Algorithm ◽  
Random Walk Model

Quartile histogram assessment of glioma malignancy using high b ‐value diffusion MRI with a continuous‐time random‐walk model

2021 ◽  
Vol 34 (4) ◽  
Author(s):  
M. Muge Karaman ◽  
Jiaxuan Zhang ◽  
Karen L. Xie ◽  
Wenzhen Zhu ◽  
Xiaohong Joe Zhou
Keyword(s):  
Random Walk ◽  
Continuous Time ◽  
Diffusion Mri ◽  
B Value ◽  
Continuous Time Random Walk ◽  
Random Walk Model ◽  
High B Value

The prediction model of personality in social networks by using data mining deep learning algorithm and random walk model

2020 ◽  
pp. 002072092093683
Author(s):  
Yu Zhu
Keyword(s):  
Data Mining ◽  
Deep Learning ◽  
Social Network ◽  
Random Walk ◽  
Prediction Method ◽  
Random Walk Model ◽  
Personality Characteristics ◽  
User Personality ◽  
Network Platform ◽  
Personality Prediction

The objective is to predict and analyze the behaviors of users in the social network platform by using the personality theory and computational technologies, thereby acquiring the personality characteristics of social network users more effectively. First, social network data are analyzed, which finds that the type of text data marks the majority. By using data mining technology, the raw data of numerous social network users can be obtained. Based on the random walk model, the data information of the text status of social network users is analyzed, and a user personality prediction method integrating multi-label learning is proposed. In addition, the online social network platform Weibo is taken as the research object. The blog information of Weibo users is obtained through crawler technology. Then, the users are labeled in accordance with personality characteristics. The Pearson correlation coefficient is used to evaluate the relation between the user personality characteristics and the user behavior characteristics of the Weibo users. The correlation between the network behaviors and personality characteristics of Weibo users is analyzed, and the scientificity of the prediction method is verified by the Big Five Model of Personality. By applying relevant technologies and algorithms of data mining and deep learning, the learning ability of neural networks on data characteristics can be improved. In terms of performance on analyzing text information of social network users, the user personality prediction method of integrated multi-label learning based on the random walk model has a large advantage. For the problem of personality prediction of social network users, through combining data mining technology and deep neural network technology in deep learning, the data processing results of social network user behaviors are more accurate.

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