I regularly forget how Bayes works. Everytime that happens I browse up to that page: https://www.bayestheorem.net/ I love the way it’s explained there.
Think Bayes: Bayesian Statistics Made Simple (2012)
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Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#12For those unclear on the concrete (rather than philosophical) difference between Bayesian and frequentist statistics in the first place, I hope it's not inappropriate for me to share this 5-minute example that I wrote a while back: https://news.ycombinator.com/item?id=11096129
This is a rather unusual book where it gives primer on probabilistic method that is actually applicable in non computer vision problems. It is Bayesian heavy and rarely touches neural networks; the book is released in 2012, the year deep learning boom started.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#13Edit typo
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#14I regularly forget how Bayes works. Everytime that happens I browse up to that page: https://www.bayestheorem.net/ I love the way it’s explained there.
How Bayes kinda works, or how I see it. Conditional probability (with some caveats that someone in the comments can fill in on): P(a,b) = P(b,a) P(a|b) * P(b) = P(b|a) * P(a) P(a|b) = P(b|a) * P(a) / P(b) a can be model and b can be data so it becomes P(model | data) = P(data | model) * P(model) / P(data) We have or can estimate the things on the right side. We want to ultimately get the thing on the left side.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#15For those unclear on the concrete (rather than philosophical) difference between Bayesian and frequentist statistics in the first place, I hope it's not inappropriate for me to share this 5-minute example that I wrote a while back: https://news.ycombinator.com/item?id=11096129
For me, this book's first chapters explained nicely about ML, MAP and Bayesian using real computer vision problems. The author included helpful visual aids (gaussian plots, contour plots, filters output, etc) http://www.computervisionmodels.com This is a rather unusual book where it gives primer on probabilistic method that is actually applicable in non computer vision problems. It is Bayesian heavy and rarely touche…
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#16My youngest has Allen Downey as a professor this year. She says he is crazy. And she means this in the best way possible. His productivity is prolific having written Think Java in 13 days. He memorized pictures and bios of all 90 students in the first year class at Olin College of Engineering. Edit typo
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
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Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#18A Bayesian pollster began with a certain set of prior probabilities. That the college educated were more likely to vote in previous elections, for example, informed the sample population, because it wouldn't make much sense to ask the opinions of those who would stay home.
Thus, based on priors that were updated with new empirical data, a new set of probabilities emerged, that gave a certain candidate a high probability of victory.
Members of the voting public, aware of this high probability, decided that this meant with certainty that this candidate would win and therefore decided to stay home on election day.
In reality the Bayesian models were incorrect as amongst other factors, a much higher number of non-college educated individuals decided to vote and to vote for the other candidate.
As it is with Bayesian intelligence, shared as much by pollsters as machine learning algorithms:
Real-time heads up display
Keeps the danger away
But only for the things that already ruined your day.Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#19The book starts from the deduction of Bayesian theorem from the first principles of logic and shows its applications to a wide range of topics. There is thorough discussion of various "paradoxes" and the author sharply criticizes the frequentist statistics. In addition there is a lot of historical references.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#20Why would you write a book that targets the Python community and ignore PEP8 styling, inconveniencing an entire community, simply because it would be too much trouble for you to change?
“Also on the topic of style, I write “Bayes’s theorem” with an s after the apostrophe, which is preferred in some style guides and deprecated in others.”
It is deprecated in all modern style guides and should not be used. You’ll get dinged in college English and writing classes for using this outdated and redundant style.
I’m sure this book is great, but, as a point of constructive criticism, I would suggest the author do a better job at adhering to the styles of code and English expected by his target audience, rather than what is comfortable for him.