usgroup

8 ms·

31.
by usgroup 1y ago
That no one doing serious statistics uses QGis is false as evidenced both by community and sponsors. Try searching “who uses QGis”.
32.
Learning from Heuristics(emiruz.com)
7 points by usgroup 1y ago|0 comments
33.
by usgroup 1y ago
The question doesn’t ask for that —- it explicitly asks us to control for over estimation of the fraction — although I rather like your interpretation as an extension.
34.
by usgroup 1y ago
See the Jeffrey posterior section here: https://en.m.wikipedia.org/wiki/Binomial_proportion_confiden... The blog post uses a non informative Jeffrey prior.
35.
by usgroup 1y ago
The Jeffrey posterior in the answer is closed form and Bayesian. The other answer is a profile likelihood. Neither involve Monte Carlo sampling. Both are general and principled.
36.
by usgroup 1y ago
I agree it could be clearer but as a general rule, if you find an interpretation under which the question doesn’t make sense, try considering another interpretation.
37.
by usgroup 1y ago
Bingo.
38.
by usgroup 1y ago
From the question: “However, it is very important that the uncertainty in the number of trials is taken into account because over-estimating a fraction is a costly mistake.“ Seems fairly clear to me that you’re supposed to use a lower bound
39.
A short statistical reasoning test(emiruz.com)
56 points by usgroup 1y ago|27 comments
40.
by usgroup 1y ago
Some less usual IQ rebuttals for those interested in the validity of the measure more generally: https://emiruz.com/post/2020-12-01-iq-rabbit-hole/
41.
A short statistical reasoning test(emiruz.com)
2 points by usgroup 1y ago|0 comments
42.
by usgroup 1y ago
https://en.wikipedia.org/wiki/Central_limit_theorem#The_gene...
43.
by usgroup 1y ago
Does the paper claim that genetics somehow drives geographic clustering? E.g. due to emigration of those carrying certain phenotypes?
44.
by usgroup 1y ago
This is a strange dichotomy. N years post CS undergraduate the majority of what you know will be self-taught, but how you've put it together in your head will be different with a theoretical background than without. A theoretical backg
45.
by usgroup 1y ago
It was very influential and had all kinds of interesting stories (e.g. https://en.wikipedia.org/wiki/Fifth_Generation_Computer_Syst... ). I'm not sure what qualifies as dead. Prolog is still around although as a sm
46.
by usgroup 1y ago
I was almost sure that Prolog would be on the list, but apparently not.
47.
by usgroup 1y ago
I'm assuming: Vendor LLM APIs + Software engineer = AI Engineer
48.
by usgroup 1y ago
Prolog seems cursed to be forgotten and re-discovered in a never ending cycle.
49.
by usgroup 1y ago
Theory is our best device for cultivating good judgement. My advise is to deeply invest in understand computer science and mathematics. Those are the foundations which will make it most likely to understand new application landscapes based
50.
by usgroup 1y ago
Probability theory. It is closely aligned to my intuition now, but when I first learned it, it was difficult to accept beyond manipulating formulae. Linearity aka most of linear algebra. Again, beyond manipulating formulae, many concepts ev
51.
by usgroup 1y ago
x ~ Binomial(N,p) and you wish to estimate p. Here are a whole collection methods for how to estimate p and calculate a confidence interval for it: https://en.wikipedia.org/wiki/Binomial_distribution#Confiden... One o
52.
by usgroup 1y ago
>frequentism largely pretends that it doesn't exist. The ongoing replication crisis shows why this is not merely pedantry, but the single most urgent issue in science. If you mean that Frequentist methods have no way of dealing with
53.
by usgroup 1y ago
It depends what the null hypothesis is here, but by construction, under a reasonable null, the p-value for an appropriate test would not be acceptable under a Frequentist framework.
54.
by usgroup 1y ago
I think Bayesian methods have made ground in sciences such as Sociology, Psychology and Ecology, which are mostly observational, but still attempt to make models with intepretable parameters. With observational studies, representing confoun
55.
by usgroup 1y ago
I consider myself an applied Statistician amongst other things, and I find this to be an ideological take mostly. When we do Statistics, we are firstly doing Applied Mathematics, which we are secondly extending to account for uncertainty fo
56.
by usgroup 1y ago
Statistical modelling is largely unrelated to machine learning in its ideology. If you're a professional Statistician then you're most likely working as part of some function heavily utilising randomised experiment design, or less
57.
Machine learning without true labels and noisy heuristics(emiruz.com)
2 points by usgroup 1y ago|0 comments
58.
by usgroup 1y ago
I think this is accurate but mostly because statistical modelling aims for interpretable parameters. That very strongly regularises complexity.
59.
Fitting models to noisy heuristic labels(emiruz.com)
3 points by usgroup 1y ago|0 comments
60.
by usgroup 1y ago
I think the SWI Prolog clpBNR package is the most complete interval arithmetic system. It also supports arbitrary constraints. https://github.com/ridgeworks/clpBNR

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