Скачать или смотреть ABCs of Conditional Independence (Detailed Edition: Hands-on Example and Beyond!)

Do you want to be a superstar in conditional independence? Then, please fear no more!
In this video, Saniya will teach you the ABC's of Conditional Independence, complete with a fully worked example testing if A and B random variables are independent given random variable C. We cover the basics and beyond! Saniya also shows applications of conditional independence, especially in Bayesian networks, and what we can also learn from random variables being conditionally independent. We work on Joint Probability Tables as well. Along the way, Saniya reviews key concepts and hopes to help build your foundation by showing how conditional independence builds on concepts from testing if 2 random variables are independent (   • Testing Independence of 2 Random Variables...  . Hopefully, by the end of this video, Conditional Independence will be easier for you, like you know your ABC's. And please don't worry if you're still stuck. Please reach out with any and all questions and Saniya is always happy to help! Please subscribe to Saniya's YouTube channel for more updates. :)

Plus, Saniya hopes you appreciate the title pun since Saniya's example uses A, B, and C random variables to help teach ye about Conditional Independence :)

Please note a quicker, shorter version of testing for conditional independence is available here:    • Conditional Independence (A and B independ...  
Bayes Theorem playlist:    • Bayes Theorem  

Biostatistics playlist:    • Biostatistics  

TIME STAMPS:
00:00 ABCs of Conditional Independence (Detailed Edition: Hands-on Example and Beyond!)
02:05 The Big Question (Hands-on Example we will work on here)
04:11 Conditional Independence: Common Cause versus Common Effect
06:27 What does Conditional Independence really mean?
13:26 Another example of Conditional Independence (Using Bayesian Networks)
14:58 Back to the Hands-On Example in this video
19:08 Marginal Independence for 2 random variables (intuition for conditional independence)
20:22 Interpreting and comparing what conditional independence looks like versus no conditional independence
22:40 Quick Review on Bayes Rule for Probability
25:48 Quick Review on Testing Independence for 2 Random Variables (e.g. Are A and B independent random variables)?
29:49 Extending concepts of independence to 3 random variables and conditional independence
40:01 8 tests to determine if A and B are conditionally independent given C
46:01 Obtaining Initial Probabilities from Joint Probability Table
01:05:36 Reminder about the 8 tests we need to do
01:06:26 Reference sheet of probabilities we have found
01:07:15 Test 1 (Fails): P(A = True | C = True) x P(B = True | C = True) = P(A = True and B = True | C = True)?
01:14:13 Since At least 1 Test Fails, A and B are NOT conditionally independent given C
==== Please note below Saniya shows the other tests for your practice and reinforcement (but in reality we would have been done now) ===================================
01:14:13 Test 2 (Fails): P(A = True | C = True) x P(B = False | C = True) = P(A = True and B = False | C = True)?
01:18:25 Test 3 (Fails): P(A = False | C = True) x P(B = True | C = True) = P(A = False and B = True | C = True)?
01:20:31 Test 4 (Fails): P(A = False | C = True) x P(B = False | C = True) = P(A = False and B = False | C = True)?
01:22:57 Test 5 (Passes): P(A = True | C = False) x P(B = True | C = False) = P(A = True and B = True | C = False)?
01:26:47 Test 6 (Passes): P(A = True | C = False) x P(B = False | C = False) = P(A = True and B = False | C = False)?
01:28:28 Test 7 (Passes): P(A = False | C = False) x P(B = True | C = False) = P(A = False and B = True | C = False)?
01:31:17 Test 8 (Passes): P(A = False | C = False) x P(B = False | C = False) = P(A = False and B = False | C = False)?
01:33:104 Summarizing the Answer: A and B are NOT independent given C in this example
01:36:01 Going Beyond: What if instead A and B ARE independent given C? What can we learn?
01:38:35 Derivation: P(B | A and C) = P(B | C) if A and B are independent given C
01:42:27 Derivation: P(A | B and C) = P(A | C) if A and B are independent given C
01:43:58 Conditional Independence is important in Bayesian Networks