A Bayesian Abstract Economy with a Measure Space of Agents by Monica Patriche

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Blind audio watermarking mechanism based on variational Bayesian learning by Xin TANG, Zhao-feng MA, Xin-xin NIU, Yi-xian YANG

“…In order to improve the performance of audio watermarking detection,a blind audio watermarking mechanism using the statistical characteristics based on MFCC features of audio frames was proposed.The spread spectrum watermarking was embedded in the DCT coefficients of audio frames.MFCC features extracted from watermarked audio frames as well as un-watermarked ones were trained to establish their Gaussian mixture models and to estimate the parameters by vatiational Bayesian learning method respectively.The watermarking was detected according to the maximum likelihood principle.The experimental results show that our method can lower the false detection rate compared with the method using EM algorithm when the audio signal was under noise and malicious attacks.Also,the experiments show that the proposed method achieves better performance in handling insufficient training data as well as getting rid of over-fitting problem.…”
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Microwave NDT/NDE Through Differential Bayesian Compressive Sensing by Marco Salucci, Lorenzo Poli, Giorgio Gottardi, Giacomo Oliveri, Luca Tosi, Andrea Massa

Subjects: “…Bayesian compressive sensing (BCS)…”
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Bayesian Pulsar Timing and Noise Analysis with Vela.jl: An Overview by Abhimanyu Susobhanan

“…We present Vela.jl , an efficient, modular, easy-to-use Bayesian pulsar timing and noise analysis package written in Julia. …”
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Analysis of Farmer Relocation Selection Behavior Based on Bayesian Network by Song Li, Yanbo Guo

“…Based on the survey data, a Bayesian network of farmers’ land use behavior is established by using Bayesian network method to investigate the choice of farmers’ migration direction. …”
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Uncertainty quantification of CT regularized reconstruction within the Bayesian framework by Negin Khoeiniha, Patricio Guerrero, Wim Dewulf

“…Given our interest in the uncertainty Quantification (UQ) of CT reconstruction, we employ statistical methods within the Bayesian framework for solving this inverse problem. …”
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IoV vertical handoff research based on Bayesian decision by Cun-qun FAN, Shang-guang WANG, Qi-bo SUN, Hua ZOU, Fang-chun YANG

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Bayesian Inference for Zero-Modified Power Series Regression Models by Katiane S. Conceição, Marinho G. Andrade, Victor Hugo Lachos, Nalini Ravishanker

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Bayesian Estimation of Archimedean Copula-Based SUR Quantile Models by Nachatchapong Kaewsompong, Paravee Maneejuk, Woraphon Yamaka

“…Four simulation studies are conducted to assess the performance of our proposed model and Bayesian estimation. Satisfactory results from simulation studies are obtained suggesting the good performance and reliability of the Bayesian method used in our proposed model. …”
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Online Bayesian Data Fusion in Environment Monitoring Sensor Networks by Yang Dingcheng, Wang Zhenghai, Xiao Lin, Zhang Tiankui

“…This paper gives a novel Bayesian model to reliably monitor physical phenomenon. …”
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Bayesian Network Approach to Customer Requirements to Customized Product Model by Qin Yang, Zhirui Li, Haisen Jiao, Zufang Zhang, Weijie Chang, Daozhu Wei

“…In order to better meet customer needs, solve the problem of incomplete and inaccurate expression, and improve the correlation between customized product performance and customer demand, a customized product method based on Bayesian network is proposed. First, the company built a custom product model based on Bayesian networks. …”
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Chain Graph Models to Elicit the Structure of a Bayesian Network by Federico M. Stefanini

“…Bayesian networks are possibly the most successful graphical models to build decision support systems. …”
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Uncertainty analysis of varying inertia estimation based on Bayesian method by Dongdong Li, Wei Hua, Yang Mi

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Bayesian Non-Parametric Mixtures of GARCH(1,1) Models by John W. Lau, Ed Cripps

“…The process is a generalisation of the Dirichlet process typically used in nonparametric models for time-dependent data provides a richer clustering structure, and its application to time series data is novel. Inference is Bayesian, and a Markov chain Monte Carlo algorithm to explore the posterior distribution is described. …”
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Bayesian location of the QCD critical point: A holographic perspective by Ratti Claudia, Grefa Joaquin, Hippert Mauricio, Manning T. Andrew, Noronha Jorge, Noronha-Hostler Jacquelyn, Vazquez Israel Portillo, Rougemont Romulo, Trujillo Michael

“…We present a Bayesian analysis, based on holography and constrained by lattice QCD simulations, which leads to a prediction for the existence and location of the QCD critical point. …”
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Bayesian Analysis of Complex Mutations in HBV, HCV, and HIV Studies by Bing Liu, Shishi Feng, Xuan Guo, Jing Zhang

Subjects: “…bayesian analysis…”
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SDN security prediction method based on bayesian attack graph by Yanshang YIN, Tongpeng SUO, Ligang DONG, Xian JIANG

“…Existing researchers use threat modeling and security analysis system to evaluate and predict SDN (software defined network) security threats, but this method does not consider the vulnerability utilization of SDN controller and the location of devices in the network, so the security evaluation is not accurate.In order to solve the above problems, according to the probability of device vulnerability utilization and device criticality, combined with PageRank algorithm, a algorithm to calculate the importance of each device in SDN was designed; according to SDN attack graph and Bayesian theory, a method to measure the success probability of device being attacked was designed.On this basis, a SDN security prediction method based on Bayesian attack graph was proposed to predict the attacker's attack path.Experimental results show that this method can accurately predict the attacker's attack path and provide more accurate basis for security defense.…”
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Research of trust evaluation model based on dynamic Bayesian network by Hong-quan LIANG, Wei WU

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The Enigma of the Tinnitus-Free Dream State in a Bayesian World by Dirk De Ridder, Kathleen Joos, Sven Vanneste

“…This can be most easily explained by a predictive Bayesian brain model. That is, during the awake state the brain constantly makes predictions about the environment. …”
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Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification by Masoud Muhammed Hassan, Halbast Rashid Ismail

Subjects: “…Bayesian deep learning…”
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