Lecture 3: Birthday Problem, Properties of Probability | Statistics 110
Harvard University・2 minutes read
The Birthday Problem explores the probability of two people sharing a birthday, with 23 individuals needed for a 50.7% chance of a match, leading to practical applications in computer science. The concept of inclusion-exclusion is discussed, with a formula involving adding individual probabilities, subtracting intersections, and adjusting for triple intersections, showcasing its practical application in probability scenarios like de Montmort's problem.
Insights
- The Birthday Problem reveals that with just 23 individuals, there is a 50.7% chance of two people sharing the same birthday, showcasing the surprising nature of probability in group settings.
- The concept of inclusion-exclusion in probability calculations is crucial, as seen in the de Montmort's problem example, where alternating sums are used to determine probabilities accurately, demonstrating the practical applications of this mathematical principle.
Get key ideas from YouTube videos. It’s free
Recent questions
What is the Birthday Problem?
The Birthday Problem analyzes the likelihood of shared birthdays.
How can the probability of a birthday match be calculated?
Calculate the probability of no match and subtract from 1.
What is the significance of "K choose 2" in the Birthday Problem?
"K choose 2" pairs are crucial for understanding probability.
How can the probability of coincidences be explained in the Birthday Problem?
Coincidences are inevitable due to numerous possibilities.
What practical applications does the Birthday Problem have?
It highlights the importance of efficient data storage.
Related videos
Harvard University
Lecture 4: Conditional Probability | Statistics 110
Quanta Magazine
2023's Biggest Breakthroughs in Math
MM AtoZ Knowledge
बिना पूछे कैसे बताये किसी का भी🎂 Happy birthday | Ganit ka jadu |😱 हैरान करने वाला जादू |
London Mathematical Society
Clutching at Random Straws, Matt Parker | LMS Popular Lectures 2010
Harvard University
Lecture 5: Conditioning Continued, Law of Total Probability | Statistics 110