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> It's difficult. Applying these techniques efficiently and correctly is way more difficult than standard frequentist methods Do you have any good resources/e
by jvans 3y ago
> It's difficult. Applying these techniques efficiently and correctly is way more difficult than standard frequentist methods
Do you have any good resources/examples for applying these methods effectively? I've read Statistical Rethinking which is a good introduction to these methods at a high level but I find when I dig into an actual problem I have a lot of gaps and wish there were more real world code examples I could learn from.
- nerdponx 3y agoI think Bayesian Data Analysis is the natural progression step. Not sure if there is a more recent book that's updated to use modern Stan examples, but the Stan user guide itself has developed into a very useful resource on its own. It contains a large number of example models and builds up concepts incrementally. The writing style is also generally easy to follow.
- jvans 3y agoI found that book impenetrable. I'm sure it's the most rigorous textbook on the subject but it is not explained in an intuitive or friendly way. I will check out the stan guide though, thanks!
- t-vi 3y agoIt knows nothing of the modern stuff (because MacKay died too early), but skipping the first parts of David MacKay: Information Theory, Inference, and Learning Algorithms you get a very accessible course in (200x) Bayesian Inference that should cover most of what you need for diving into PPL applications. http://www.inference.org.uk/mackay/itila/book.html http://www.inference.org.uk/mackay/itila/book.html
- nerdponx 3y agoIn my case, I used it in an actual course on Bayesian inference. Looking back over the material it doesn't seem particularly complicated for anyone with a solid probability background, but maybe the concepts are hard if you aren't seeing them presented nicely in a lecture setting.