Can you have a model without data? (+ R code) скачать в хорошем качестве

Can you have a model without data? (+ R code)

This smooth integration of, and transition from, the prior knowledge we incorporate into the model to the information augmented by the data we collect is one reason why I think Bayesian thinking is better suited for scientific modeling. A more natural accumulation of knowledge, erasing the boundaries between what we already know (which should be considered a form of “data” itself) and hard data collected on a spreadsheet. It changes the way you approach the problem: instead of focusing right away on the data, which will be exhausted once it’s used, you focus on conceptualizing the living, breathing model of the world that generated it, and thus allow data to only contribute to that model as seen fit. 00:00 Introduction 03:14 A summary passage 04:16 Importing the data in RStudio 05:17 Frequentist confidence intervals 08:25 Specifying the prior distribution 09:37 Deriving the posterior distribution 11:18 Posterior at accumulating sample sizes Blog link: https://www.centralstatz.com/posts/ca...