Bayesian Computational Statistics: Advanced Inference
Master Bayesian inference, from fundamentals to advanced computation. Learn MCMC methods, hierarchical models, and practical R implementation.
Master Bayesian inference, from fundamentals to advanced computation. Learn MCMC methods, hierarchical models, and practical R implementation.
This rigorous course provides a comprehensive introduction to Bayesian Statistical Inference and Data Analysis. Students will explore prior and posterior distributions, Bayesian estimation and testing, and advanced computational methods. The curriculum covers single and multiparameter models, large-sample inference, hierarchical models, and regression analysis. Practical implementation using R software enhances theoretical understanding. By course completion, students will have a strong foundation in Bayesian statistics and its computational aspects, preparing them for advanced statistical analysis in various fields.
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What you'll learn
Understand and apply Bayesian inference principles
Implement MCMC methods for complex statistical models
Develop hierarchical and regression models in a Bayesian framework
Perform large-sample inference and evaluate frequency properties
Use R for Bayesian computation and data analysis
Apply Bayesian methods to real-world statistical problems
Skills you'll gain
This course includes:
10.93 Hours PreRecorded video
32 assignments
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FullTime access
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There are 9 modules in this course
This comprehensive course offers a rigorous introduction to Bayesian Statistical Inference and Data Analysis. Students will explore fundamental concepts such as prior and posterior distributions, Bayesian estimation and testing, and advanced computational methods. The curriculum progresses from single-parameter models to complex multiparameter and hierarchical models, covering large-sample inference, regression analysis, and mixture models. Practical implementation using R software enhances theoretical understanding, preparing students for advanced statistical analysis in various fields.
Fundamentals of Bayesian Inference
Module 1 · 9 Hours to complete
Single Parameter Models
Module 2 · 11 Hours to complete
Multiparameter Models
Module 3 · 10 Hours to complete
Large-Sample Inference and Frequency Properties
Module 4 · 10 Hours to complete
Bayesian Computation
Module 6 · 12 Hours to complete
Regression Models
Module 7 · 11 Hours to complete
Advanced Topics
Module 8 · 8 Hours to complete
Summative Course Assessment
Module 9 · 3 Hours to complete
Fee Structure
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Instructor
Prof.
Shahrzad Jamshidi is an academic at Illinois Institute of Technology, where she teaches courses such as Bayesian Computational Statistics and Statistical Learning. Her focus is on applying statistical methods and computational techniques to solve complex problems, particularly in the fields of applied mathematics and data science. Dr. Jamshidi's research interests include statistical modeling and data analysis, where she explores innovative methodologies to enhance understanding and application of statistical theories. She is dedicated to providing students with a solid foundation in statistical principles, preparing them for careers in data analysis, research, and various applications across industries. Through her courses, she aims to equip learners with the necessary skills to analyze data effectively and make informed decisions based on statistical insights.
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