# Bayesian Statistics: Techniques and Models, 1.02 (R) Module 1 Assignments and Materials

Assignments This module has three required quizzes and an honors quiz. A score of 75% is required to pass (80% for the honors quiz). Quizzes can be attempted up to four times in an eight-hour period. Additional Materials In addition to regular lectures and quizzes, this module includes the following materials. Lesson 1: Statistical Modeling Discussion prompt: Read what your peers have to say about the prompt and share your ideas on the discussion board. Lesson 2: Bayesian Modeling Supplementary reference: This optional reading reviews probability distributions for common discrete and continuous random variables that will be used throughout the course. Lesson 3: Monte Carlo Estimation Code: This document contains the development for the screen-recording lectures, including the code used in the examples. Background reading for Lesson 4: This reading introduces Markov chains, providing vital background for Module 2. A basic background in Markov chains is necessary to understand how Markov chain Monte Carlo works.

Assignments This module has three required quizzes and an honors quiz. A score of 75% is required to pass (80% for the honors quiz). Quizzes can be attempted up to four times in an eight-hour period. Additional Materials In addition to regular lectures and quizzes, this module includes the following materials. Lesson 1: Statistical Modeling Discussion prompt: Read what your peers have to say about the prompt and share your ideas on the discussion board. Lesson 2: Bayesian Modeling Supplementary reference: This optional reading reviews probability distributions for common discrete and continuous random variables that will be used throughout the course. Lesson 3: Monte Carlo Estimation Code: This document contains the development for the screen-recording lectures, including the code used in the examples. Background reading for Lesson 4: This reading introduces Markov chains, providing vital background for Module 2. A basic background in Markov chains is necessary to understand how Markov chain Monte Carlo works.