Tutorial in Bayesian Statistics Part 2: Parameter estimation and practice

2020.06.24 Presenter: Gianni Galbiati Part 2 covered Bayesian parameter estimation with a practical emphasis. The first section briefly covered parameter estimation as a statistical paradigm for scientific inference and software options for doing it in Python. The second section was a hands-on tutorial using PyMC3 to complete analyses in worksheets. Prerequisites: basic probability theory, basic frequentist statistics, and if you like to participate in the hands-on tutorial, Python and Git basics Slides: https://www.cns.nyu.edu/malab/static/files/tutorial/bayesian_tutorial_20200624.pdf Slides with notes: https://docs.google.com/presentation/d/1OMLTBOSkeJB4pd9BgAl6rQYLNYvQydlzHwwTZgi5MUk/edit#slide=id.p Code example (presidential heights): https://colab.research.google.com/drive/1UzlnsH_RJiBSu3nMngLH4MwGii_WKMgC?usp=sharing Code example (Aspen’s change detection task): https://colab.research.google.com/drive/1dibSGcrZZXdawXU9q_jFwudM1FRUAPsw?usp=sharing