samch93

RIP Professor Winston!

I really enjoyed your AI lectures. For example, the SVM lecture (https://www.youtube.com/watch?v=_PwhiWxHK8o=_PwhiWxHK8o=_Pwh...), is by far the best explanation of SVMs I've ever heard, highly recommended!

Wow, incredible. I feel like an alien watching this strange planet called earth.

Highly recommend the novel which provides the basis for this movie „Traumnovelle“ by Arthur Schnitzler (https://de.m.wikipedia.org/wiki/Traumnovelle). The novel seems to be heavily influenced by Freudian psycho analysis, I also think the location of Vienna at the beginning of the 20th century is very interesting.

Recently a good friend gave me a small Galton board for my birthday and it stands now on my desk. It is so cool to do a little "simulation" and see the magic of the central limit theorem. Highly recommended as a gift for any statistically interested person!

Obligatory xkcd https://xkcd.com/1782/

I am surprised by how many people equal frequentist statistics with Neyman-Pearson hypothesis testing. In my opinion, the main difference between the two approaches being whether the parameters of a statistical model are considered as fixed or random, everything else follows from this.

On the subject of statistical education: The point I tried to make is that I think it is much easier to study first the likelihood, the central quantity of frequentist inference. One can then go to the Bayesian world simply by allowing the parameters to be random variables. Furthermore, as other commentors have pointed out, technical difficulties arise in the non-conjugate Bayesian setting when MCMC sampling has to be used. In my opinion, MCMC algorithms, convergence diagnostics, etc. are certainly not topics for an intro stats course.

As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a major role in obtaining the posterior distribution. In my opinion, this progression is much more natural than starting fully Bayesian.

I am a happy owner of an SE and I am afraid of the moment it breaks down. Can anyone recommend an alternative compact phone with similar build quality and the same features in terms of software and hardware?