Bayesian Computational Statistics

This course is part of Advanced Statistical Techniques for Data Science Specialization

Instructor: Shahrzad (Sarah) Jamshidi

Skills you'll gain

  •   Statistical Modeling
  •   Markov Model
  •   Statistical Methods
  •   Probability
  •   Statistical Inference
  •   Statistical Programming
  •   Regression Analysis
  •   Probability Distribution
  •   R Programming
  •   Data Analysis
  •   Bayesian Statistics
  •   Statistical Software
  •   Statistical Analysis
  •   Simulations
  • There are 9 modules in this course

    Required Textbook: Gelman, A., Carlin, J. B., Stern, H. S., Rubin, D. B. (2013) Bayesian Data Analysis, Third Edition, Chapman & Hall/CRC. Software Requirements: R or Python, Word processing (such as Word, Pages, LaTeX, etc)

    Module 2: Single Parameter Models

    Module 3: Multiparameter Models

    Module 4: Large-Sample Inference and Frequency Properties

    Module 5: Hierarchical Models

    Module 6: Bayesian Computation

    Module 7: Regression Models

    Module 8: Advanced Topics

    Summative Course Assessment

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