STAT 465
Bayesian Statistics Spring 2024
Division III Quantitative/Formal Reasoning

Class Details

Prior knowledge being constantly updated by empirical observations — the essence of Bayesian thinking provides a natural, intuitive, and more importantly, mathematically sounded, probabilistically principled way to characterize the process of learning. With some of its key ideas formulated based on Bayes’ Theorem dating back to 18th century, Bayesian inference is one of oldest schools of statistics (more than a century earlier than the Frequentist!). Yet it was not until the recent developments in sampling algorithms and computational powers that Bayesian inference gained its revival. Bayesian, and Bayesian-based methods, with their flexibilities in modeling (generative) process of data, interpretability with posterior probability statements, and coherent principles to incorporate empirical evidence a priori, have played key roles in modern data analysis, especially for those “big data” with enhanced complexity and connectivity. This course is designed to provide students a comprehensive understanding to what is Bayesian and the how’s and why’s. Students will be introduced to classic Bayesian models, basic computational algorithms/methods for Bayesian inference, as well as their applications in various fields, and comparisons with classic Frequentist methods. As Bayesian inference finds its roots and merits particularly in application, this course puts great emphasis on enhancing students’ skills in statistical computation (mostly with R) and data analysis.
The Class: Format: lecture
Limit: 20
Expected: 15
Class#: 3978
Grading: yes pass/fail option, yes fifth course option
Requirements/Evaluation: Homework, exams, and project
Prerequisites: MATH/STAT 341, STAT 346, and STAT 360, or permission of instructor
Enrollment Preferences: seniors, Statistics majors
Distributions: Division III Quantitative/Formal Reasoning
QFR Notes: This course utilizes mathematics and computer-based tools for the Bayesian approach for analyzing data and making statistical inferences.

Class Grid

Updated 3:17 am

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