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所在平台: Udemy |
课程主页: https://www.udemy.com/course/bayesian-statistics-w/
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课程名称：全面指南：贝叶斯统计 课程概述： 本课程是贝叶斯统计的全面指南，内容包括视频讲解、实际案例、示例、数值问题、学习笔记、练习册、测验等。课程涵盖了概率论和贝叶斯建模的基本理论，以及它们在数据科学、商业和应用科学中的常见问题中的应用。课程分为以下几个部分： 第一和第二部分： 这两部分涵盖了理解贝叶斯统计基础的重要概念——对统计推断/推论统计的概述、贝叶斯概率介绍、频率派/经典推断与贝叶斯推断的对比、贝叶斯定理及其在贝叶斯统计中的应用、贝叶斯统计的实际例子、先验与后验分布的关键概念、先验的类型、解决实际数值问题以计算总体参数的后验概率分布、共轭先验和杰弗里非信息先验。 第三部分： 该部分涵盖了贝叶斯统计中的区间估计：频率推断中的置信区间与贝叶斯推断中的可信区间、置信区间和可信区间的解读、后验均值的可信区间计算。 第四部分： 该部分涵盖了贝叶斯假设检验：贝叶斯因子的介绍、贝叶斯因子的解读、解决数值问题以获得两个竞争假设的贝叶斯因子。 第五部分： 该部分涉及贝叶斯统计中的决策理论：贝叶斯决策理论的基础与示例、决策理论术语（状态/参数空间、行动空间、决策规则、损失函数）、贝叶斯决策理论的实际例子、分类损失矩阵、最小化期望损失、频率与贝叶斯方法的决策、不同损失函数的类型（平方误差损失、绝对误差损失、0-1损失）、贝叶斯期望损失、风险（频率风险/风险函数、贝叶斯估计、贝叶斯风险）、决策规则的可接受性、贝叶斯估计和贝叶斯风险的求解程序（正态与广泛形式的分析）、不同损失函数的贝叶斯估计和贝叶斯风险的数值问题解决。 第六部分： 该部分包含：贝叶斯的辩护与批评、贝叶斯统计在各种领域中的应用、附加资源、额外讲座和测验。 课程结束时，您将全面理解贝叶斯概念，从基础开始，掌握有效使用贝叶斯方法和进行概率思考的能力。注册本课程将使您在考试中取得优异成绩或在其他地方应用贝叶斯方法变得更加轻松。完成本课程，掌握原则，加入全球顶尖统计学生的行列。
This course is a comprehensive guide to Bayesian Statistics. It includes video explanations along with real life illustrations, examples, numerical problems, take away notes, practice exercise workbooks, quiz, and much more. The course covers the basic theory behind probabilistic and Bayesian modelling, and their applications to common problems in data science, business, and applied sciences. The course is divided into the following sections:Section 1 and 2: These two sections cover the concepts that are crucial to understand the basics of Bayesian Statistics- An overview on Statistical Inference/Inferential StatisticsIntroduction to Bayesian ProbabilityFrequentist/Classical Inference vs Bayesian InferenceBayes Theorem and its application in Bayesian StatisticsReal Life Illustrations of Bayesian StatisticsKey concepts of Prior and Posterior DistributionTypes of PriorSolved numerical problems addressing how to compute the posterior probability distribution for population parameters Conjugate PriorJeffrey's Non-Informative PriorSection 3: This section covers Interval Estimation in Bayesian Statistics:Confidence Intervals in Frequentist Inference vs Credible Intervals in Bayesian InferenceInterpretation of Confidence Intervals & Credible IntervalsComputing Credible Interval for Posterior MeanSection 4: This section covers Bayesian Hypothesis Testing:Introduction to Bayes FactorInterpretation of Bayes FactorSolved Numerical problems to obtain Bayes factor for two competing hypotheses Section 5: This section caters to Decision Theory in Bayesian Statistics:Basics of Bayesian Decision Theory with examplesDecision Theory Terminology: State/Parameter Space, Action Space, Decision Rule. Loss FunctionReal Life Illustrations of Bayesian Decision TheoryClassification Loss MatrixMinimizing Expected LossDecision making with Frequentist vs Bayesian approachTypes of Loss Functions: Squared Error Loss, Absolute Error Loss, 0-1 LossBayesian Expected LossRisk: Frequentist Risk/Risk Function, Bayes Estimate, and Bayes RiskAdmissibility of Decision RulesProcedures to find Bayes Estimate & Bayes Risk: Normal & Extensive Form of AnalysisSolved numerical problems of computing Bayes Estimate and Bayes Risk for different Loss FunctionsSection 6: This section includes:Bayesian's Defense & CritiqueApplications of Bayesian Statistics in various fieldsAdditional ResourcesBonus Lecture and a QuizAt the end of the course, you will have a complete understanding of Bayesian concepts from scratch. You will know how to effectively use Bayesian approach and think probabilistically. Enrolling in this course will make it easier for you to score well in your exams or apply Bayesian approach elsewhere.Complete this course, master the principles, and join the queue of top Statistics students all around the world.
