Advanced Bayesian Statistics Using R from edX

Advanced Bayesian Data Analysis Using R is part two of the Bayesian Data Analysis in R professional certificate.

This course is directed at people who are already familiar with the fundamentals of Bayesian inference. It explores further the concepts, methods, and algorithms introduced in the part one (Introductory Bayesian Data Analysis Using R).

The course places mixed effects regression models useful for experiments with repeated measures or additional hierarchy often encountered in biostatistics, ecology and health sciences among others within the Bayesian context. It takes a closer look at the Markov Chain Monte Carlo (MCMC) algorithms, why they work and how to implement them in the R programming language. Convergence assessment and visualisation of the results are discussed in some detail. The course also explores Bayesian model averaging, often used in machine learning, all within the context of practical examples.

Finally, we discuss different kinds of missing data, and the Bayesian methods of dealing with such situations.

Prior facility in basic algebra and calculus as well as programming in R is highly recommended.

What you'll learn

• Using latent (unobserved) variables and dealing with missing data.

• Multivariate analysis within the context of mixed effects linear regression models. Structure, assumptions, diagnostics and interpretation. Posterior inference and model selection.

• Why Monte Carlo integration works and how to implement your own MCMC Metropolis-Hastings algorithm in R.

• Bayesian model averaging in the context of change-point problem. Pinpointing the time of change and obtaining uncertainty

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Develops advanced Bayesian data analysis skills using the R programming language, highly relevant for scientific research and data-driven decision-making

Builds on foundational knowledge in Bayesian inference, making it suitable for students with some prior exposure to the subject

Taught by Elena Moltchanova, an expert in Bayesian statistics who provides valuable insights and practical guidance

Emphasizes practical applications, with real-world examples to illustrate the concepts and methods

Requires prior knowledge of basic algebra, calculus, and R programming, which may pose a barrier for some students

Assumes familiarity with introductory Bayesian data analysis concepts, which may not be suitable for complete beginners

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Advanced Bayesian Statistics Using R with these activities:

Review Calculus and Linear Algebra

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Go over basic calculus and linear algebra concepts to prepare yourself for the mathematical aspects of the course.

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Review limits, derivatives, and integrals.
Review matrices, vectors, and linear equations.
Solve practice problems to test your understanding.

Attend Study Group

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Join a study group to discuss course concepts, collaborate on assignments, and learn from your peers. This will provide a supportive learning environment and enhance your understanding of the material.

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Find a study group or create your own.
Meet regularly to discuss course material and work on assignments.
Share knowledge and perspectives with group members.
Provide feedback and support to others in the group.

Recall Bayesian Concepts

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Re-familiarize yourself with basic Bayesian concepts to refresh your memory and prepare for the course material.

Browse courses on Bayesian Statistics

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Review the concepts of probability and distributions.
Go through Bayes' theorem and its applications.
Solve simple Bayesian inference problems.

Four other activities

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Read Bayesian Data Analysis

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Read and review the foundational book on Bayesian data analysis to deepen your understanding of the subject.

View Bayesian Data Analysis (Chapman & Hall/CRC... on Amazon

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Acquire a copy of the book.
Read and make notes on the relevant chapters.
Summarize the key concepts and techniques.
Apply your understanding to examples and exercises.

Simulating Markov Chains

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Practice simulating Markov chains with different transition matrices. This will deepen your understanding of the underlying principles and prepare you for more complex Bayesian modeling.

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Review the basic concepts of Markov chains.
Create transition matrices for simple Markov chains.
Implement Markov chain simulation algorithms, such as the Metropolis-Hastings algorithm.
Analyze the simulated data to verify the properties of Markov chains.

Test Stationarity of Data

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Familiarize yourself with different methods for testing and verifying the stationarity of time series. This will help you build a strong foundation for your Bayesian analysis.

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Review the concept of stationarity and its importance in Bayesian time series analysis.
Explore the Augmented Dickey-Fuller (ADF) test and KPSS test.
Apply theADF and KPSS tests to real-world time series datasets
Interpret the test results and draw conclusions about the stationarity of the time series.

Build a Bayesian Regression Model

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Develop a Bayesian linear regression model to predict a continuous outcome variable. This hands-on project will reinforce your understanding of Bayesian inference and model building.

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Choose a dataset with a continuous outcome variable.
Define the Bayesian regression model and specify the prior distributions.
Fit the model using MCMC methods and evaluate the convergence.
Interpret the posterior distributions and make predictions.

Career center

Learners who complete Advanced Bayesian Statistics Using R will develop knowledge and skills that may be useful to these careers:

Statistician

A Statistician uses mathematics and statistics to collect, analyze, and interpret data. They work in a variety of fields, including healthcare, finance, and government. The Advanced Bayesian Statistics Using R course can help you develop the skills needed to be a successful Statistician. This course will teach you the concepts, methods, and algorithms for Bayesian inference, which is a powerful tool for data analysis. You will also learn how to use the R programming language to implement Bayesian methods.

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Data Analyst

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Data Scientist

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Operations Research Analyst

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Quantitative Analyst

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Actuary

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Epidemiologist

An Epidemiologist studies the distribution and determinants of health-related states or events in specified populations. They use this information to develop and evaluate public health programs. The Advanced Bayesian Statistics Using R course can help you develop the skills needed to be a successful Epidemiologist. This course will teach you the concepts, methods, and algorithms for Bayesian inference, which is a powerful tool for analyzing epidemiological data. You will also learn how to use the R programming language to implement Bayesian methods.

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Biostatistician

A Biostatistician applies statistical methods to the analysis of biological data. They work in a variety of fields, including medicine, public health, and pharmaceuticals. The Advanced Bayesian Statistics Using R course can help you develop the skills needed to be a successful Biostatistician. This course will teach you the concepts, methods, and algorithms for Bayesian inference, which is a powerful tool for analyzing biological data. You will also learn how to use the R programming language to implement Bayesian methods.

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Market Researcher

A Market Researcher conducts surveys, interviews, and other research to gather data about consumers and markets. They use this data to help businesses understand their customers and make better decisions. The Advanced Bayesian Statistics Using R course can help you develop the skills needed to be a successful Market Researcher. This course will teach you the concepts, methods, and algorithms for Bayesian inference, which is a powerful tool for data analysis. You will also learn how to use the R programming language to implement Bayesian methods.

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Software Engineer

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Financial Analyst

A Financial Analyst analyzes financial data to make investment recommendations. They work in a variety of industries, including banking, investment management, and insurance. The Advanced Bayesian Statistics Using R course may be helpful for Financial Analysts who want to use Bayesian inference to analyze financial data. This course will teach you the concepts, methods, and algorithms for Bayesian inference. You will also learn how to use the R programming language to implement Bayesian methods.

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Physicist

A Physicist studies the laws of nature. They work in a variety of industries, including academia, government, and industry. The Advanced Bayesian Statistics Using R course may be helpful for Physicists who want to use Bayesian inference to analyze scientific data. This course will teach you the concepts, methods, and algorithms for Bayesian inference. You will also learn how to use the R programming language to implement Bayesian methods.

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Economist

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Computer Scientist

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