SUMMARY
The discussion focuses on calculating the conditional expectations \(\mathbb{E}[X \vert 1_{\{X+Y=0\}}]\) and \(\mathbb{E}[Y \vert 1_{\{X+Y=0\}}]\) for independent Bernoulli random variables \(X\) and \(Y\) with parameter \(p\). The key conclusion is that \(\mathbb{E}[X+Y \vert 1_{\{X+Y=0\}}] = 0\), derived from the formula \(\mathbb{E}[X+Y \vert 1_{\{X+Y=0\}}] = \frac{\mathbb{E}[(X+Y)1_{\{X+Y=0\}}]}{\mathbb{P}[X+Y=0]} = 0\). The indicator function \(1_{\{X+Y=0\}}\) is defined as 1 if \(X+Y=0\) and 0 otherwise, leading to the conclusion that \(\mathbb{P}[X=0 \vert X+Y=0] = 1\).
PREREQUISITES
- Understanding of Bernoulli random variables
- Knowledge of conditional expectation
- Familiarity with indicator functions
- Basic probability theory concepts
NEXT STEPS
- Study the properties of conditional expectations in probability theory
- Learn about indicator random variables and their applications
- Explore the concept of independence in random variables
- Investigate the implications of the law of total expectation
USEFUL FOR
Students and professionals in statistics, data science, and probability theory who are looking to deepen their understanding of conditional expectations and their applications in real-world scenarios.