贝叶斯预测模型在气温变化预测中的应用

贝叶斯预测模型是运用贝叶斯统计方法进行的一种预测 ,贝叶斯统计不同于一般统计方法 ,其不仅利用模型信息和数据信息 ,而且充分利用了样品的先验信息。根据贝叶斯预测模型的特点 ,介绍了几个贝叶斯预测模型的预测过程和计算步骤 ,并对南宁地区自 1 970年以来 1 9年间的气温变化进行了预测。根据计算结果 ,分析了不同贝叶斯预测模型的预测特点 ,并给出了几点结论。

贝叶斯预测模型的评价

本文提出有效超前期概念,并给出确定它的贝叶斯方法;

贝叶斯预测模型在矿物含量预测中的应用

矿物含量预测是资源评价中经常遇到的问题,贝叶斯预测模型是运用贝叶斯统计方法进行的一种预测。贝叶斯统计不同于一般的统计方法,其不仅利用模型信息和数据信息,而且充分利用先验信息。根据贝叶斯预测模型的特点,介绍了几个贝叶斯观测模型的预测过程和计算步骤,并对白鹿塬水家嘴剖面的mg2+含量进行了观测,根据计算结果,分析了不同贝叶斯预测模型的预测特点,并给出了几点结论。

基于贝叶斯动态预测模型的商品推荐方法

传统的电子商务推荐系统虽然考虑到个性化的推荐,但不能很好的描述用户行为,使得个性化的推荐略显不足。本文提出基于贝叶斯动态预测的模型,并结合Agent技术,很好地建立了用户行为预测模型。该方法以用户历史数据为基础,并结合用户的实时行为建立用户行为预测模型。本文将此方法运用于商品推荐系统中,实验证明此方法能高效地为客户产生个性化的商品推荐集合,优于某些传统方法。

基于动态贝叶斯网络的光伏发电短期概率预测

采用确定性的预测方法对光伏发电量进行预测,在光伏出力波动较大时误差较大,且无法展示预测时刻可能出现的所有情况及其出现的概率。针对确定性预测方法的不足,提出一种条件概率预测方法,应用动态贝叶斯网络(dynamic Bayesian network,DBN)理论,建立光伏发电预测的DBN模型。该模型考虑影响光伏发电量的多种因素,及各因素之间的相互联系,基于当前时刻各影响因素水平的条件下,预测未来短期光伏发电量的概率分布。该分布能够给出比较全面的光伏发电信息,为调度人员提供运行指导。最后采用实际系统进行分析,结合多种验证指标对预测结果进行评估,结果表明,所提方法是正确合理的,能够较好地预测短期内未来时刻光伏发电量的概率分布。

基于CT增强图像影像组学特征模型预测肺鳞癌和腺癌价值初探

目的评估基于CT增强图像的影像组学方法在肺腺癌(Lung Adenocarcinoma,ADC)和肺鳞状细胞癌(Squamous Cell Carcinoma,SCC)中的鉴别能力。方法回顾性分析泰州市人民医院2017年1月至2019年12月经病理证实的51例ADC患者和34例SCC患者,从CT增强图像感兴趣区中提取影像组学特征。按照7:3的比例,选取59个患者作为训练集,26个作为验证集。应用相关性检验、单因素方差分析或秩和检验、单因素Logistic回归分析、多随机森林算法交叉验证的方法选择特征及降维,采用多因素Logistic回归方法和贝叶斯网络构建预测模型进行比较,通过受试者操作特征(Receiver Operating Characteristic,ROC)曲线的曲线下面积(Area Under the Curve,AUC)评价模型效能(灵敏度、特异度、准确率)。结果8个特征通过Logistic回归分析方法建立模型,通过ROC曲线发现训练集AUC为0.97、灵敏度为83.3%、特异度为97.1%、准确率为91.5%。验证集AUC为0.89、灵敏度为80.2%、特异度为73.3%、准确率为84.6%。结论基于Logistic回归的影像组学方法在SCC与ADC的预测与鉴别中具有较高的应用价值,可为临床医师决策提供科学依据。

Forecasting model of residential load based on general regression neural network and PSO-Bayes least squares support vector machine

Firstly,general regression neural network(GRNN) was used for variable selection of key influencing factors of residential load(RL) forecasting.Secondly,the key influencing factors chosen by GRNN were used as the input and output terminals of urban and rural RL for simulating and learning.In addition,the suitable parameters of final model were obtained through applying the evidence theory to combine the optimization results which were calculated with the PSO method and the Bayes theory.Then,the model of PSO-Bayes least squares support vector machine(PSO-Bayes-LS-SVM) was established.A case study was then provided for the learning and testing.The empirical analysis results show that the mean square errors of urban and rural RL forecast are 0.02% and 0.04%,respectively.At last,taking a specific province RL in China as an example,the forecast results of RL from 2011 to 2015 were obtained.

Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

In this paper, we provide a Word Emotion Topic （WET） model to predict the complex word e- motion information from text, and discover the dis- trbution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories： joy, love, expectation, sur- prise, anxiety, sorrow, anger and hate. We use a hi- erarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without con- sidering any complicated language features. Our ex- periment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields（CRFs） on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.

Time-series gas prediction model using LS-SVR within a Bayesian framework

The traditional least squares support vector regression（LS-SVR）model,using cross validation to determine the regularization parameter and kernel parameter,is time-consuming.We propose a Bayesian evidence framework to infer the LS-SVR model parameters.Three levels Bayesian inferences are used to determine the model parameters,regularization hyper-parameters and tune the nuclear parameters by model comparison.On this basis,we established Bayesian LS-SVR time-series gas forecasting models and provide steps for the algorithm.The gas outburst data of a Hebi 10th mine working face is used to validate the model.The optimal embedding dimension and delay time of the time series were obtained by the smallest differential entropy method.Finally,within a MATLAB7.1 environment,we used actual coal gas data to compare the traditional LS-SVR and the Bayesian LS-SVR with LS-SVMlab1.5 Toolbox simulation.The results show that the Bayesian framework of an LS-SVR significantly improves the speed and accuracy of the forecast.

Global and Regional Trends and Projections of Infective Endocarditis-Associated Disease Burden and Attributable Risk Factors from 1990 to 2030

Objective To forecast the future burden and its attributable risk factors of infective endocarditis(IE).Methods We analyzed the disease burden of IE and its risk factors from 1990 to 2019 using the Global Burden of Disease 2019 database and projected the disease burden from 2020 to 2030 using a Bayesian age-period-cohort model.Results By 2030,the incidence of IE will increase uncontrollably on a global scale,with developed countries having the largest number of cases and developing countries experiencing the fastest growth.The affected population will be predominantly males,but the gender gap will narrow.The elderly in high-income countries will bear the greatest burden,with a gradual shift to middle-income countries.The incidence of IE in countries with middle/high-middle social-demographic indicators(SDI) will surpass that of high SDI countries.In China,the incidence rate and the number of IE will reach 18.07 per 100,000 and 451,596 in 2030,respectively.IEassociated deaths and heart failure will continue to impose a significant burden on society,the burden on women will increase and surpass that on men,and the elderly in high-SDI countries will bear the heaviest burden.High systolic blood pressure has become the primary risk factor for IE-related death.Conclusions This study provides comprehensive analyses of the disease burden and risk factors of IE worldwide over the next decade.The IE-associated incidence will increase in the future and the death and heart failure burden will not be appropriately controlled.Gender,age,regional,and country heterogeneity should be taken seriously to facilitate in making effective strategies for lowering the IE disease burden.