Furthermore, there is no evidence that anyone else had the idea of using thermionic valves to greatly increase the speed of the code breaking computer, so if Tommy Flowers didn't exist it probably wouldn't have been discovered until much later (i.e. too late to help the war effort).
Of course CEO's such as Bill Gates and Steve Jobs have made important individual contributions to society, but they were not as pivotal as that of Tommy Flowers, and furthermore they have been rewarded in money (many would say too much), whereas Tommy Flowers has received very little reward or recognition for his achievement.
Exactly, in your words: "being built by a lot of people", i.e. not just Bill Gates and Steve Jobs. If Bill Gates & Steve Jobs didn't exist those employees would be working for different computer companies such as IBM, Olivetti, Apricot, Xerox (who invented the point & click windows system) or one of many others, and we would have similar products under different names.
The geopolitical achievement of winning the war (and the consequences of that) was the whole purpose of building the machine, so it doesn't make sense to dismiss it as you did.
The value that companies such as Microsoft & Apple provide is supplied by all their thousands of employees, not just their CEO's. So its not a fair comparison to compare the output of Microsoft & Apple with the output of a single person (Tommy Flowers). Furthermore there are plenty of alternatives to Microsoft & Apple: if Bill Gates & Steve Jobs didn't exist we'd be probably be running Unix, Linux or one of the many other operating systems that lost out to Windows & MacOS for market share. If Tommy Flowers didn't exist we might have lost the war.
In zsh you can use the _gnu_generic function for simple completion of commands with a --help flag.
Just put a line like this somewhere in your startup file: compdef _gnu_generic <CMD>
and if anyone is interested in delving more deeply into the statistical concepts & results referenced in the paper of this post (e.g. VC-dimension, PAC-learning, etc), I can recommend this book: https://amzn.eu/d/7Zwe6jw
I had a look at Eric Stansifer's write-up of his decision, but I didn't read all of it (83 pages!).
He does seem to have a good understanding of Bayesian decision making and hypothesis testing.
What confuses me however, is his dismissal of two pieces of evidence in table 2 which he says should be ignored "following the presumption that HSM is the first SSE", and yet earlier, in footnote 24, he states "We are very specifically NOT conditioning on that place being HSM" (talking about the first SSE location).
Can anyone enlighten me about this seeming contradiction?
Another point: while both judges are qualified scientists, their expertise is in microbiology/virology not epidemiology, but it is the epidemiological aspect of the situation that is the most contentious part of the analysis, and AFAIK the part that swung the decision in favour of zoonotic origins for both judges. Without prior assumptions they both agree that the DNA evidence favours the lab leak theory.
Which means this; it gives further weight to the lab leak theory, and shows the reasoning behind it.
I don't have time to watch the 3hr debate or read all of that article (which makes some misrepresentative statements, and like your response, is rather venomous in tone), but here is the response from rootclaim about the debate outcome: https://blog.rootclaim.com/covid-origins-debate-response-to-...
I also know from experience that scientists, and people in general, are often not well trained in the kind of probabilistic reasoning that is required for combining and weighing up multiple sources of evidence.
This article is very interesting and informative, however it's a bit ironic that an article about misinterpretations of the meaning of the p-value, misinterprets the misinterpretation; in the first blue box it's clear that Bernstein is interpreting the p-value as the probability of randomly rejecting the null (which is what you do when you get something statistically significant) yet in the text following that they say he's interpreting it as the probability of the null.
Bernsteins mistake is that he appears to interpret it as an unconditional probability rather than a conditional one (correct interpretation; p-value = Prob(rejecting the null when the null is true)).
@justk The R^2 value of 0.01 calculated on that webpage uses both states, not just one: the variance of the predicted values across both states is 0.55^3+0.45^3 - (0.55^2+0.45^2) ≃ 0.497 ≃ 0.5
I don't think it makes sense to use a mixed model in this case since the variance is the same for each state. A mixed model is used when the observations have some structured heteroskedasticity, i.e. different variances for different values of the independent variables.
@justk What you're talking about might make sense if there were more independent variables to consider, but in this case there's only one, state.
So in fact you could say that there are two conditional linear models in the example; one for the first state (state=0), and one for the second (state=1).
The model does the best job with the information available (state).