|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/strategic-economic-decision-making/
课程评论：没有评论
课程名称：战略经济决策制定 课程概述：Grover Group, Inc. (GGI) 提供本课程，以帮助学习者在影响组织经济结果的商业决策中运用归纳逻辑。本课程基于我们2016年的入门书籍《战略经济决策制定手册：使用贝叶斯信念网络进行复杂决策》的内容，扩展自2013年出版的《战略经济决策制定：使用贝叶斯信念网络进行复杂决策》。本课程将深入探讨贝叶斯信念网络（BBN），并提供与贝叶斯定理相关的基本原理及其在BBN中的应用。 贝叶斯信念网络的价值在于，它们能够根据初始的概率猜测，通过可观察的信息进行筛选，以预测未来的状态，得出后验概率。本课程主要面向非统计学专业的学习者，同时也适合对统计学和贝叶斯定理有基本了解的人士。课程中，我们将通过真实世界的应用案例，引导学习者建模和应用BBN。 课程将介绍离散数学的基本原理，包括集合论和离散概率公理，涵盖计数以及先验、边际、似然、联合和最终的后验概率计算。课程结束时，学习者将能够基于经济领域的实际问题复制10个BBN。我们将阐释拟合贝叶斯模型的要求，使学习者能够数学上确定后验概率，这些后验概率将代表调查者的初始猜测。本领域关于离散贝叶斯理论的文献相对较少，因此本课程将吸引对BBN几乎没有了解的非统计学者，以及目前在工程、计算机、生命科学和社会科学领域开展研究的统计学家。
Grover Group, Inc. (GGI), offers this course so that learners can use inductive logic when making business decisions that effect an organizations economic outcomes. We base this course on our primer, "A Manual for Strategic Economic Decision-Making: Using Bayesian Belief Networks to make Complex Decisions (2016)," which is an extension of "Strategic Economic Decision-Making: Using Bayesian Belief Networks to make Complex Decisions (Springer, 2013). This course is a thorough investigation on Bayesian belief networks (BBN), where we will provide the learner with the underlying principles associated with Bayes' theorem and its application to BBN. The value of BBNs is that they take an initial guess of probability likelihoods and filter them through observable information to predict future states of nature in the form of posterior probabilities. This course is meant for learners that are non-statisticians and will complement those that have a basic understanding of statistics and Bayes' theorem. During this course, we will walk the learner through the modeling and application of BBN using real-world applications. We will do this by introducing the learner to the underlying principles of discrete mathematics using set theory and discrete axioms of probability, These underlying concepts include counting and subsequent calculation of prior, marginal, likelihood, joint, and finally posterior probabilities. At the end of the course, the learner will replicate 10 BBNs based on real world problems in the area of economics. We will explain the requirements of fitting a Bayes' model in this course. Upon course completion, the learner can mathematically determine posterior probabilities. These posteriors will represent the initial guess of the investigator. Very little has been published in the area of discrete Bayes' theory, and this course will appeal to both non-statisticians with little to no knowledge of BBN and statisticians currently conducting research in the fields of engineering, computing, life sciences, and social sciences.
