Discovering Stable Robot Grasps for Unknown Objects in Presence of Uncertainty Using Bayesian Models

Siddiqui, Muhammad Sami; Coppola, Claudio; Solak, Gokhan; Jamone, Lorenzo

doi:10.1007/978-3-030-89177-0_5

Cited by 1 publication

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References 16 publications

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“…e best assumption is the most likely hypothesis because there are prior probabilities for various hypotheses h on the data D to be observed, and h is the hypothesis space that contains the objective function. e Bayesian algorithm (BA) has many probability classes method and an optimal method for predicting the class of unknown samples [13][14][15], widely used in data deep search, image processing, bioinformatics and multitarget retrieval of information, and other elds. Look at how the conditions are collected in the data set, and nd out which data belong to the di erent categories using how the conditions are collected.…”

Section: Network Security Risk Quantificationmentioning

confidence: 99%

Application of Bayesian Algorithm in Risk Quantification for Network Security

Lei

2022

Computational Intelligence and Neuroscience

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Network security risk quantification involves both technical and management aspects. Risk quantification has great uncertainty and cannot be fully quantified. Therefore, the fully objective realization of network information security risk quantification is not yet mature. This paper analyzes and quantifies the network security risks caused by various threat sources through a network security risk quantification model based on the Bayesian algorithm. By combining expert knowledge, the conditional probability matrix under the inference rule of the Bayesian algorithm is clarified, and the subjective judgment information of experts on the damage degree of the target information system is synthesized into the prior information system of network security threat. The Bayesian algorithm is used to realize the observation node of objective assessment information and combining subjective security threat levels to achieve continuity and accumulation of security assessments. The error is about 3%, which has a very good effect on the quantification of network security risk.

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Section: Network Security Risk Quantificationmentioning

confidence: 99%

Application of Bayesian Algorithm in Risk Quantification for Network Security

Lei

2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

Discovering Stable Robot Grasps for Unknown Objects in Presence of Uncertainty Using Bayesian Models

Cited by 1 publication

References 16 publications

Application of Bayesian Algorithm in Risk Quantification for Network Security

Application of Bayesian Algorithm in Risk Quantification for Network Security

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