This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation. Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.
カリフォルニア大学サンタクルーズ校（University of California, Santa Cruz）
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- 4 stars25.58%
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BAYESIAN STATISTICS: FROM CONCEPT TO DATA ANALYSIS からの人気レビュー
This course is vary helpful for the understanding of the basics of Bayesian analysis. The course material are fantastic as well as the teacher. Good introductory Course in My opinion.
Very nice introduction to bayesian concepts and rationale. With this course I could understand why should I spend more time learning this technique (which I will definetly do on sequence).
the notes for the lectures are missing. In my opinion the notes, which includes the video materials could be very useful.\n\nthe course was good. I learnt some new concepts in bayesian thinking.
Very clear and informative. Would like a more extensive and combined reference material (PDF, so less need to lookup e.g. definitions of effective sample size for various distributions).
This Specialization is intended for all learners seeking to develop proficiency in statistics, Bayesian statistics, Bayesian inference, R programming, and much more. Through four complete courses (From Concept to Data Analysis; Techniques and Models; Mixture Models; Time Series Analysis) and a culminating project, you will cover Bayesian methods — such as conjugate models, MCMC, mixture models, and dynamic linear modeling — which will provide you with the skills necessary to perform analysis, engage in forecasting, and create statistical models using real-world data.
What are the pre-requisites for this course?
What computing resources are expected for this course?