- Bayesian Statistics
- Data Science
- R Programming
- Data Analysis
- Statistics
- Bayesian Inference
- Gibbs Sampling
- Markov Model
- Mixture Model
- Forecasting
- Dynamic Linear Modeling
- Time Series
贝叶斯统计 专项课程
Bayesian Statistics for Modeling and Prediction. Learn the foundations and practice your data analysis skills.
提供方
您将学到的内容有
Bayesian Inference
Time Series Forecasting
Hierarchical Modeling
您将获得的技能
关于此 专项课程
应用的学习项目
This Specialization trains the learner in the Bayesian approach to statistics, starting with the concept of probability all the way to the more complex concepts such as dynamic linear modeling. You will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data, and then dive deeper into the analysis of time series data.
The courses in this specialization combine lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience, while the culminating project is an opportunity for the learner to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you know to real-world data. You will review essential concepts in Bayesian statistics, learn and practice data analysis using R (an open-source, freely available statistical package), perform a complex data analysis on a real dataset, and compose a report on your methods and results.
Prior experience with calculus (you don’t need to remember how to do it, just to understand the concepts); an introductory statistics course.
Prior experience with calculus (you don’t need to remember how to do it, just to understand the concepts); an introductory statistics course.
专项课程的运作方式
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此专项课程包含 5 门课程
Bayesian Statistics: From Concept to Data Analysis
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.
Bayesian Statistics: Techniques and Models
This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our “Bayesian toolbox” with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution. We will use the open-source, freely available software R (some experience is assumed, e.g., completing the previous course in R) and JAGS (no experience required). We will learn how to construct, fit, assess, and compare Bayesian statistical models to answer scientific questions involving continuous, binary, and count data. This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. The lectures provide some of the basic mathematical development, explanations of the statistical modeling process, and a few basic modeling techniques commonly used by statisticians. Computer demonstrations provide concrete, practical walkthroughs. Completion of this course will give you access to a wide range of Bayesian analytical tools, customizable to your data.
Bayesian Statistics: Mixture Models
Bayesian Statistics: Mixture Models introduces you to an important class of statistical models. The course is organized in five modules, each of which contains lecture videos, short quizzes, background reading, discussion prompts, and one or more peer-reviewed assignments. Statistics is best learned by doing it, not just watching a video, so the course is structured to help you learn through application.
Bayesian Statistics: Time Series Analysis
This course for practicing and aspiring data scientists and statisticians. It is the fourth of a four-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, Techniques and Models, and Mixture models.
提供方

加州大学圣克鲁兹分校
UC Santa Cruz is an outstanding public research university with a deep commitment to undergraduate education. It’s a place that connects people and programs in unexpected ways while providing unparalleled opportunities for students to learn through hands-on experience.
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