DEPENDENT PARAMETERS DEGRADATION RELIABILITY ASSESSMENT BASED ON JEFFREYS NONINFORMATIVE PRIOR PARAMETERS by YIN ZeKai, GUO Yu

“…The correlation between the parameters of the Gamma degradation process was described by the Jeffreys uninformative prior distribution. And the Bayesian model was used to obtain the full conditional distribution of each parameter. …”
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Node localization algorithm based on kernel function and Markov chains by ZHAO Fang1, LUO Hai-yong2, LIN Quan3, MA Yan4

“…To position indoor objects accurately and robustly,a novel node localization based on kernel function and Markov chains was presented,which employs Bayesian filter framework and radio fingerprinting technology.It uses kernel function to construct likelihood function to take full advantage of the similarity between observation and several training samples,which avoids the error brought by employing a priori determined distribution model.Furthermore,the proposed algorithm uses Markov chains to improve the localization accuracy and shorten the positioning time.It limits the search space of the matching grids with object’s previous state and the environment layout,and refuses the object’s impossible position jump during the moving process.Experiments confirm that the proposed localization outperforms the algorithm with Gaussian distribution model.…”
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Equilibrium and Welfare Analysis in Second-Price Auctions with Resale and Costly Entry by Xiaoyong Cao, Yunxia Yang, Yuntao Yang, Siru Li

“…We characterize the perfect Bayesian equilibrium in cutoff strategies and identify sufficient conditions under which the equilibrium is unique. …”
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A New Extension of the Exponentiated Weibull Model Mathematical Properties and Modelling by Majdah Mohammed Badr, Amal T. Badawi, Alya S. Alzubidi

“…A medical dataset was utilized to evaluate the practical importance of the EW-EW distribution using additional criteria such as the Akaike information criterion (AKINC), the correct AKINC (COAKINC), the Bayesian INC (BINC), and the Hannan-Quinn INC (HQINC). …”
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Decision-level fusion model of multi-source intrusion detection alerts by LI Zhi-dong, YANG Wu, WANG Wei, MAN Da-peng

“…In order to lessen the dependence on training samples significantly and eliminate rigorous constraint conditions,an alert fusion model that supports online incremental training was presented.Firstly,primary alerts vector was mapped to voting pattern,so as to reduce statistical space.Then,the conditional probability distributions of various voting patterns in normal or attack traffic were inferred via training.Afterwards,according to the variation of statistical characteristics,the composition of the traffic being detected was inferred instantly.Finally,fusion decision was made via threshold constraint method and Bayesian inference.Besides extended applicative scope,the model proposed can track and adapt to the traffic being detected well,and improve detection performance significantly only via small scale training.…”
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SuperKEKB positron beam tuning using machine learning by Natsui Takuya

“…Automatic tuning is realized using Bayesian optimization and the downhill simplex method. …”
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Multilayer neural network model for unbalanced data by Xue ZHANG, Zhiguo SHI, Xuan LIU

“…Classification of unbalanced data often has low performance of the classifier because of the unbalance of data between classes.Using AUC (the area under the ROC curve) as evaluation index,combined with one class F-score feature selection and genetic algorithm,a multilayer neural network model was established,and a more favorable feature set for unbalanced data classification was selected,so as to establish a deeper model suitable for classification of unbalanced data.Based on Tensor Flow,a multilayer neural network model was established.Using four different UCI datasets for testing,and comparing with the traditional machine learning algorithms such as Naive Bayesian,KNN,neural networks,etc,the performance of the proposed model built on the unbalanced data classification is more excellent.…”
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Research of a spam filter based on improved naive Bayes algorithm by 曹翠玲, 王媛媛, 袁野, 赵国冬

“…In spam filtering filed,naive Bayes algorithm is one of the most popular algorithm,a modified using support vector machine(SVM)of the native Bayes algorithm :SVM-NB was proposed.Firstly,SVM constructs an optimal separating hyperplane for training set in the sample space at the junction two types of collection,Secondly,according to its similarities and differences between the neighboring class mark for each sample to reduce the sample space also increase the independence of classes of each samples.Finally,using naive Bayesian classification algorithm for mails.The simulation results show that the algorithm reduces the sample space complexity,get the optimal classification feature subset fast,improve the classification speed and accuracy of spam filtering effectively.…”
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Recovering Decay Rates from Noisy Measurements with Maximum Entropy in the Mean by Henryk Gzyl, Enrique Ter Horst

“…We show how to obtain an estimator with the noise filtered out, and using simulated data, we compare the performance of our method with the Bayesian and maximum likelihood approaches.…”
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WIND SPEED ANALYSIS AT IKEJA, NIGERIA USING THE CONVENTIONAL PROBABILITY DENSITY FUNCTIONS by OLUSEYI OGUNSOLA, OFURE OSAGIEDE

“…The best fit test for these PDFs were determined from Akaike Information Criteria, Bayesian Information Criteria, Kolmogorov-Smirnov test, Cramer-von Mises statistics, Anderson-Darling Statistic, Mean Square Error and Chi-Square Test using Maximum Likelihood Estimation and Method of Moments as parameter estimates. …”
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The Beta-Lindley Distribution: Properties and Applications by Faton Merovci, Vikas Kumar Sharma

“…Further, we also discuss estimation of the unknown model parameters in both classical and Bayesian setup. The usefulness of the new model is illustrated by means of two real data sets. …”
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A distributed expectation maximization-principal component analysis monitoring scheme for the large-scale industrial process with incomplete information by Xuanyue Wang, Xu Yang, Jian Huang, Xianzhong Chen

“…Finally, the process monitoring and fault detection results are fused by Bayesian inference technique. Case studies on the Tennessee Eastman process is applied to show the effectiveness and performance of our proposed approach.…”
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Integrated Forecast of Monthly Saltwater Intrusion at Modaomen Waterway Based on Multiple Models by LU Pengyu, LIN Kairong, YANG Yugui, YUAN Fei, HE Yong

“…This paper builds the regression model by Random Forest (RF) algorithm, Support Vector Machine (SVM) and Elman Neural Network (ENN), and conducts a monthly integrated forecast through Bayesian Model Averaging (BMA) method. The results indicate that: ① RF, SVM and ENN show different extent of uncertainty on small sample sets; ② The simulation accuracy of BMA is significantly improved, with NSE of 0.67, which is 22%, 24% and 33% higher than that of RF, SVM and ENN, respectively.…”
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Link quality prediction model based on Gaussian process regression by Jian SHU, Manlan LIU, Yaqing SHANG, Yubin CHEN, Linlan LIU

“…Link quality is an important factor of reliable communication and the foundation of upper protocol design for wireless sensor network.Based on this,a link quality prediction model based on Gaussian process regression was proposed.It employed grey correlation algorithm to analyze correlation between link quality parameters and packet receive rate.The mean of the link quality indication and the mean of the signal-to-noise were selected as input parameters so as to reduce the computational complexity.The above parameters and packet receive rate were taken to build Gaussian process regression model with combination of covariance function,so that link quality could be predicted.In the stable and unstable scenarios,the experimental results show that the proposed model has better prediction accuracy than the one of dynamic Bayesian network prediction model.…”
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Evaluation of Provincial Economic Resilience in China Based on the TOPSIS-XGBoost-SHAP Model by Zhan Wu

“…The aim of this research is to propose a framework for measuring and analysing China’s economic resilience based on the XGBoost machine learning algorithm, using Bayesian optimization (BO) algorithm, extreme gradient-boosting (XGBoost) algorithm, and TOPSIS method to measure China’s economic resilience from 2007 to 2021. …”
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Hierarchical game based spectrum access optimization for anti-jamming in UAV network by Chaoqiong FAN, Chenglin ZHAO, Bin LI

“…For the anti-jamming spectrum access optimization problem in unmanned aerial vehicle (UAV) communication networks,considering the complex and diverse malicious jamming from jammers,a Bayesian Stackelberg game was proposed to formulate the competitive relations between UAV users and jammers.Specifically,jammers acted as the leader,whereas users acted as followers of the proposed game.Based on their different utility functions,the jammer and users independently and selfishly selected their optimal strategies and obtained the optimal channels selection.Due to the NP-hard nature,it was challenging to obtain the Stackelberg Equilibrium of the proposed game.To this end,a hierarchical learning framework was formulated,and a hierarchical channel selection-learning algorithm was proposed.Simulations demonstrate that with the proposed hierarchical learning algorithm,UAV nodes can adjust their channel selection and obtain superior performance.…”
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Research and application of XGBoost in imbalanced data by Ping Zhang, Yiqiao Jia, Youlin Shang

“…At the same time, the optimal parameters are automatically searched and adjusted through the Bayesian optimization algorithm to realize classification prediction. …”
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The circular economy and sustainable development in the European Union's new member states by Mihaela Simionescu

“…A macroeconomic approach based on panel data models and Bayesian random linear regression models was conducted for Cyprus, Latvia, Lithuania, Hungary, Poland, Romania, Slovenia, and Slovakia from 2008-2020. …”
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Inference of Process Capability Index Cpy for 3-Burr-XII Distribution Based on Progressive Type-II Censoring by Rashad M. EL-Sagheer, Mustafa M. Hasaballah

“…Also, bootstrap confidence intervals (CIs) of the estimators have been obtained. The Bayesian estimates for the index Cpy have been obtained by the Markov Chain Monte Carlo method. …”
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Exact Posterior distribution of risk ratio in the Kumaraswamy–Binomial model by Andrade, Jose A. A., Rathie, Pushpa

“…In categorical data analysis, the $2\times 2$ contingency tables are commonly used to assess the association between groups and responses, this is achieved by using some measures of association, such as the contingency coefficient, odds ratio, risk relative, etc. In a Bayesian approach, the risk ratio is modeled according to a Beta-Binomial model, which has exact posterior distribution, due to the conjugacy property of the model. …”
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