John Maynard Smith's Evolutionary Genetics is a classic textbook. The second edition has simulation/programming exercises after every chapter. Have fun :)

How did they come up with the likelihood distribution? Maybe they sampled 100 products from each machine and for each sample counted the number of faulty products. Maybe they sampled 1.000.000 products from each machine...

We don't know which sample size is used: the likelihood distribution doesn't reveal this.

Implicitly, they have an infinite sample size, because the distribution on P(B|A_1) is infinitely precise. Suppose we also wanted to learn P(B|A_1) from the history of the factory: then we might model it as having a fixed rate of defective outputs, and the probability we assign to particular defect rates is a beta distribution. We might start off with a Jeffreys prior and then update as we see the tool produce defective or normal products, eventually ending up with, say, a beta(5.5,95.5) for tool A_1.

Exercise for the reader: given that hyperparameter distribution for P(B|A_1) (and similar ones for A_2 and A_3), do we need the full hyperparameter distribution for all three tools to determine the probability that a known defective output came off of A_1, or can we get the same answer using only a handful of moments from each distribution?

Huh? Harry thought it was McGonagall. What in this chapter changes that?

Aww, so Dumbledore was the one who told Harry to look for Hermione on the train in chapter 6 :)

Donated $100.

Could use an editor or feedback of some kind for a planned series of articles on scarcity, optimization, and economics. Have first four articles written and know what the last article is supposed to say, and will be filling in the gaps for a while. Would like to start posting said articles when there is enough to keep up a steady schedule.

No knowledge of economics required, but would be helpful if you were pretty experienced with how the community likes information to be presented. Reply to this comment or send me a message, and let me know how I can send you the text of the articles.

To tl;dr a tl;dr

• Seeds (read: grains) have the highest concentration of "don't eat me" toxins, because of the role they play in reproduction; phytic acid, for instance, inhibits absorption of several minerals.

• Humans can live off vegetables and some fish (Kitavans) or almost entirely meat (Inuit) and be pretty healthy. However, even animals optimized for eating seeds, much less humans, cannot live off grains exclusively without developing pellegra and beriberi.

• Cereals have exceptionally high energy density, which may lead to overfeeding.

• It's plausible grains interfere with satiety responses via endocrine disruption

• Grains have a bad omega-3 : omega-6 ratio.

• Grains have poor nutrient density.

Distilling Lindeberg's object-level advice: eat lean meat, fish, vegetables (including root vegetables), fruit, and nuts (but not too many). Do not eat grains, dairy, sugar, beans, or processed things. Drink water. I've written about how he comes to this (and my reservations about the lack of extant evidence to support any strong recommendations, including his) previously.

if lots of people start/stop eating meat, the short-run supply curve will look different 5 years from now due to that change alone.

NO!

I'm curious, given my credentials with what probability do you think I'm right?

Edit: I'm embarrassed by this, but I retract what I said.

If you look at net worth counterfactuals, the person deciding whether to borrow money to buy BTC is facing the same decision as someone who is already in debt and is deciding whether to buy BTC or use the same money to pay off some of their debt. If you think leveraged investments in BTC are unwise, you should also categorically advise people with any amount of debt to not buy BTC.

I haven't looked at the empirical evidence because I didn't think it would be as convincing as the 2 theoretical arguments

Heh. It seems we have pronounced... methodological differences :-D