A Bayesian Model of Hurricane Trajectories

Feb 21, 2022
PDF
Photo by ChatGPT

In this project, I implemented a Bayesian model using the Metropolis–Hastings algorithm to achieve a stationary distribution in a Markov Chain. I estimated parameters and the numerical standard error and constructed the 95% confidence interval from the results. The model was used to predict the spatial moving trends and wind speed of hurricanes.

Statistics Bayesian Statistics Metropolis–Hastings Markov Chain
刘宗超
刘宗超
Ph.D student in epidmeiology and biostatistics

My research interests include biostatistics, cancer epidemiology, health data science, and computational biology. Currently, I mainly focus on gastric cancer-related topics.