I am a rising junior at Hathaway Brown School in Shaker Heights, Ohio. When I'm not cracking enjoyably difficult math problems or debugging some code, I can be found at debate tournaments, at a biomedical engineering research lab at Case Western Reserve University, singing Carnatic classical music, or doing Indian classical dance. I enjoying learning, and I aspire to get better at everything I do.
Project: Determining the Weight of a Loaded Coin Using Various Algorithms
Bayesian inference and expectation maximization aim to determine the weight of various coins. Bayesian inference numerically evaluates the likelihood (or "belief") that certain guesses for the weight of the coin might be correct; expectation maximization utilizes estimated probabilities in order to determine the weight of multiple coins by calculating the likelihood of production of each stream, which, in turn, is utilized to progressively refine the estimates until convergence. This Demonstration visualizes the implementation of both of these algorithms to depict the differences in the approach. For the Bayesian inference simulation, the weight of the coin and the number of coin flips (number of trials) can be manipulated, and for the expectation maximization simulation, the weight of the two coins, the initial estimate for the weight, and the number of ten-flip trials can be manipulated.