An Intro to PyMC and the Language for Describing Statistical Models

In our previous article on <a href="https://pub.towardsai.net/why-most-introductory-examples-of-bayesian-statistics-misrepresent-it-d2e12ac69278?gi=f468b252c728" rel="noopener ugc nofollow" target="_blank">why most examples of Bayesian inference misrepresent what it is</a>, we clarified a common misunderstanding among beginners of Bayesian Statistics. That is, the field of Bayesian Statistics IS NOT defined by its use of Bayes’ Theorem but rather by its use of probability distributions to characterize uncertainty and consider the full range of possible outcomes. So, for example, rather than being told that a given medical device is 95% effective in detecting a disease given the fact that you know with absolute certainty you are already infected (what we can otherwise call our True Positive Rate (TPR)), we can consider scenarios where that device is only 24%, 69%, or 91% effective in detecting the disease and how those numbers change your probability of contracting it given that you have tested positive from the device.  <a href="https://pub.towardsai.net/an-intro-to-pymc-and-the-language-for-describing-statistical-models-ac9d56c363c0">Click Here</a>