贝叶斯预测模型

贝叶斯预测模型在气温变化预测中的应用

The Application of Bayes ′ prediction model in the prediction of temperature variety

观测数据分析中几种方法的探讨（一）回归&时间序列模型和贝叶斯预测模型

DISCUSSION ON SEVERAL NEW METHODS FOR ANALYZING OBSERVATIONAL DATA OF DAMS Part One Regression-time Series Analysis and Bayesian Model

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。

On the base of the calculating results , the prediction characters of different Bayes ′ prediction model are analyzed , and some conclusions are given .

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1970年以来19年间的气温变化进行了预测。

According to the characters of Bayes ′ prediction model , the prediction process and calculating steps of several Bayes ′ prediction models are introduced , and the air temperature variety among 19 years in Nanning area since 1970 is predicted .

以某桥为例，运用ANSYS的零阶优化法，采用贝叶斯气温预测模型预测了拱肋节段安装温度，计算了不计温差影响和计入温差影响的节段预抬量和索力。

Take one bridge as an example , using zero-order optimistic method of ANSYS , adopt temperature forecasting model Byes to forecast the installment temperature for arch rib segment . Calculate the cable force and pre-camber of considering the influence of temperature and without considering the influence of temperature .

本文基于相互作用蛋白对的二级结构信息，提出一个蛋白质相互作用的贝叶斯分类器预测模型。

In this study , a new approach was introduced to predict interaction of proteins solely by analyzing their secondary structures , and the predicting model was built based on Bayesian classifier .

基于贝叶斯网络增强预测模型的人脸多特征跟踪

Bayesian Network Enhanced Prediction Based Multiple Facial Feature Tracking

进一步比较各种方法的预测效果，发现贝叶斯动态线性预测模型的预测效果更好，值得推荐。

The further comparison among the results of several forecasting methods makes it worth recommending the Bayesian dynamically lineal forecasting model .

先后提出了基于贝叶斯网络增强预测模型的跟踪方法和基于时空概率图模型的跟踪方法。

We propose a Bayesian network enhanced prediction based multiple facial feature tracking algorithm and a spatiotemporal graphical model based one .

为了进一步提高辨识精度，减少计算量，提出基于序列中碱基的组成信息以及位置信息的改进贝叶斯剪接位点预测模型。

In order to improve the identification accuracy and reduces computational complexity further , according to the composition and position information of bases in the sequence , an improved naive Bayesian splice site classification is proposed .

因而本文详细研究了可靠性工程，统计学和信息工程等相关领域的知识，结合维修备件需求的特点，提出了一套完整的系统解决方案且成功构建了基于贝叶斯网络的预测模型。

Hence this dissertation investigated throughout all related theories that may improve service parts requirement forecast in reliability engineering , statistics and information technologies . Then it introduced a full system solution of service parts requirement forecast and successfully built up the forecast model based on Bayesian Network .

通过模型对比分析，结果证明，包含贝叶斯算法的负荷预测模型对复杂的负荷特性和新样本数据有着更加出色的学习能力。

Through the model analysis , the results show that the load forecasting model containing the Bayesian algorithm has more excellent learning ability for complex load characteristics and new sample data .

本文首先介绍了贝叶斯统计方法、贝叶斯预测思想及其国内外研究现状，并在此基础上，详细介绍了贝叶斯预测模型，并对贝叶斯预测模型，特别是贝叶斯常均值模型做了深入的研究。

The paper firstly introduces the Bayesian statistical methods , Bayesian forecasting thought and research at home and abroad . Furthermore , the paper introduces various different Bayesian forecasting models in details , especially Bayesian Constant Mean Model ( BCM Model ) which is deeply studied .