Introduction to Bayesian Data Analysis
Introduction to Bayesian Data Analysis
This course introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence. Upon completion of this course, students should be able to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective.
Introduction: Why are Bayesian methods important for data analysts?
Here are some of the advantages of Bayesian methods over the standard frequentist approach used in data analysis:
- Prior knowledge/expertise can be incorporated into the data analysis
- Models can be flexibly specified to reflect the assumed generative process
- The results of the analysis – the posterior distributions of the parameters of interest – have an intuitive interpretation
- Hypothesis testing can be carried out in a more meaningful manner than the standard used null hypothesis significance testing
Prerequisites: Who is this course for?
We assume the following in this course:
- Basic familiarity with the programming language R, openHPI offers a free R course for Beginners (in German)
- Experience with data analysis using linear models
- It is helpful (but not necessary) to have had some exposure to linear mixed models using the R library lme4
- High-school mathematics (pre-calculus)
- Some basic concepts from probability theory (sum and product rule, conditional probability)
This course is not appropriate for participants who don't know R programming and who have no experience at all with data analysis.
Course outcomes: What will you learn from this course?
- Some basic ideas relating to random variables
- Some fundamental properties of probability distributions
- Application of Bayes' rule in data analysis
- The concept of likelihood and its role in Bayesian statistical modeling
- Bayesian regression models using brms (a front-end for Stan)
- How to visualize and interpret prior and posterior distributions
- How to generate prior and posterior predictive distributions for evaluating models
- How to interpret the results of simple regression models
After completing this course, you will be in a good position to learn how to use more advanced Bayesian methods, such as hierarchical models, finite mixture models, multinomial processing tree models, measurement error models, etc.
Duration: Not defined
Level: Beginner
Certification: Yes
Cost: Free
Language: English
Type: Self-Paced
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