W1_L7: Conditioning with multiple discrete random variables

Welcome to Week 1 Lecture 7 of the course "Statistics for Data Science - II" by Prof. Andrew Thangaraj. Full Course: https://study.iitm.ac.in/ds/course_pages/BSMA1004.html Video Overview In this lecture, we delve into the concept of conditioning with multiple discrete random variables. Building upon the idea of marginalization, we explore how conditioning acts as a bridge, connecting marginals to provide a comprehensive understanding of the joint distribution. You’ll learn how to express conditional PMFs using the formula “joint divided by marginal” and see how this extends to cases with more than two random variables. We also discuss factoring a joint PMF to simplify complex calculations and gain insights into stochastic phenomena — an essential tool for building models and calculating probabilities. About IIT Madras' online Bachelor of Science programme IIT Madras offers four-year BS programmes that aim to provide quality education to all, irrespective of age, educational background, or location. The BS programme has multiple levels, which provide flexibility to students to exit at any of these levels. Depending on the courses completed and credits earned, the learner can receive a Foundation Certificate from IITM CODE (Centre for Outreach and Digital Education), Diploma(s) from IIT Madras, or BSc/BS Degrees from IIT Madras. For more details, Visit: https://www.iitm.ac.in/academics/study-at-iitm/non-campus-bs-programmes #Conditioning #DiscreteRandomVariables #Probability #Marginalization #JointPMF #ConditionalPMF #ProbabilityTheory #Statistics #RandomVariables #Factoring #StochasticProcesses #Mathematics #DataScience #ModelBuilding #Uncertainty