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研究经历ResearchExperienceBayes’ReasoningandBayesianInference贝叶斯推理和贝叶斯(主义)推断ClassicalBayes’Prediction经典的贝叶斯预测FromClassicalBayes’PredictiontoLikelihoodPrediction从经典的的贝叶斯预测到似然预测MaximumLikelihoodCriterion=MaximumGeneralizedKLInformationCriterion最大似然准则=最大广义KL信息准则BayesianInference:AdvantagesandDisadvantages贝叶斯主义推断:优点和缺点TwoReasonsforLogicalBayesianInference需要逻辑贝叶斯推断的两个理由UsingtheTruthFunctionorMembershipFunctionT(θj|X)astheInferenceTool用真值函数或隶属函数作为推断工具WhyDoWeUseTruthFunctionT(θj|X)insteadofReverseLikelihoodFunctionP(θj|X)为什么要用真值函数而不是反似然函数?TheThirdKindofBayes’TheoremIProposed我提出第三种贝叶斯定理IllustratingBayes’TheoremIII图解贝叶斯定理IIISematicInformationMeasuresOptimizingTruthFunctionswithMaximumSemanticInformationCriterion用最大语义信息准则优化真值函数ComparingBIandLBI比较贝叶斯推断和逻辑贝叶斯推断Application1:Multi-labelLearningandClassificationApplication2:MaximumMutualInformationClassificationsforUnseenInstancesChannels’Matching(CM)IterativeAlgorithm:AnExampleShowsItsReliability.信道匹配迭代算法:一个例子显示其可靠性Application3:MixtureModelsCM-EMAlgorithmforMixtureModelsAnExampleagainstEMConvergenceProofComparingCM-EMwithEMandMMAlgorithms2223Summary总结