Introduction to graduate stats in particle physics and astronomy. Frequentist and Bayesian perspectives. Distributions. Central limit theorem. Likelihoods. Parameter estimation. Goodness of fit. Hypothesis testing. Priors. Monte-Carlo techniques. Machine learning. Applications may include rare-event searches, and star and galaxy clustering (3.0 credit units). PREREQUISITE: Permission from the course coordinator EXCLUSION: PHYS 849
Introduction to graduate stats in particle physics and astronomy. Frequentist and Bayesian perspectives. Distributions. Central limit theorem. Likelihoods. Parameter estimation. Goodness of fit. Hypothesis testing. Priors. Monte-Carlo techniques. Machine learning. Applications may include rare-event searches, and star and galaxy clustering (3.0 credit units). PREREQUISITE: Permission from the course coordinator EXCLUSION: PHYS 849
