Haskell/GHC will soon have an extension for linear types, which will bring the languages much closer: http://blog.tweag.io/posts/2017-03-13-linear-types.html
A confidence interval won't adjust the points (point estimates) but will give those points with a lower sample size wide confidence intervals (often covering zero). Using an (empirical) Bayesian multilevel model can…
It seems to me more bent on buttressing a pro-scientific religious viewpoint.
Panama papers -- <anger> OpenBazaar -- cool!
Just so people know, there is a competing/complementary approach to causality in statistics, called the potential outcomes or (Neyman-)Rubin causal model, which as I understand it is currently more popular than Pearl's…
I don't think machine learning vs Bayes vs sampling theory has much to do with the content of the article, which is more about causality than interpretations of probability. > it's far more likely that coffee causes…
Possibly related simultaneous discussion: https://news.ycombinator.com/item?id=10328699
+1. A fantastic article. Definitely one of the best popular-level stats pieces I've read.
Is anyone really an AGI expert?
“Seeing the world through the lens of Bayes’ Theorem is like seeing The Matrix. Nothing is the same after you have seen Bayes.” I'm pretty sure this is an instance of cognitive bias.
A great article on similar issues is "Turing Test: 50 Years Later": http://crl.ucsd.edu/~saygin/papers/MMTT.pdf It touches on the "physics is computable therefore the mind is computable", and similar such arguments, in…
I prefer comprehensions over either mapping or explicit recursion. Mapping is a win for abstraction but a loss for clarity.
I cried.
Also a review at NDPR, which makes some points similar to mine above: http://ndpr.nd.edu/news/53333-philosophy-between-the-lines-t...
There are some distinct ideas that this article at times seems to conflate: - Obscurity, speaking in fables, non-clarity, indirectness. Multiple layers to one's teaching. This possibly for the sake of pedagogy. -…
Haskell/GHC will soon have an extension for linear types, which will bring the languages much closer: http://blog.tweag.io/posts/2017-03-13-linear-types.html
A confidence interval won't adjust the points (point estimates) but will give those points with a lower sample size wide confidence intervals (often covering zero). Using an (empirical) Bayesian multilevel model can…
It seems to me more bent on buttressing a pro-scientific religious viewpoint.
Panama papers -- <anger> OpenBazaar -- cool!
Just so people know, there is a competing/complementary approach to causality in statistics, called the potential outcomes or (Neyman-)Rubin causal model, which as I understand it is currently more popular than Pearl's…
I don't think machine learning vs Bayes vs sampling theory has much to do with the content of the article, which is more about causality than interpretations of probability. > it's far more likely that coffee causes…
Possibly related simultaneous discussion: https://news.ycombinator.com/item?id=10328699
+1. A fantastic article. Definitely one of the best popular-level stats pieces I've read.
Is anyone really an AGI expert?
“Seeing the world through the lens of Bayes’ Theorem is like seeing The Matrix. Nothing is the same after you have seen Bayes.” I'm pretty sure this is an instance of cognitive bias.
A great article on similar issues is "Turing Test: 50 Years Later": http://crl.ucsd.edu/~saygin/papers/MMTT.pdf It touches on the "physics is computable therefore the mind is computable", and similar such arguments, in…
I prefer comprehensions over either mapping or explicit recursion. Mapping is a win for abstraction but a loss for clarity.
I cried.
Also a review at NDPR, which makes some points similar to mine above: http://ndpr.nd.edu/news/53333-philosophy-between-the-lines-t...
There are some distinct ideas that this article at times seems to conflate: - Obscurity, speaking in fables, non-clarity, indirectness. Multiple layers to one's teaching. This possibly for the sake of pedagogy. -…