My introduction to Bayesian probability was accidentally reinventing it while trying to invent my own AI system. It naturally followed by constructing a network of information which could be queried to get back whatever had been fed into it and perform deduction/induction.
Think Bayes: Bayesian Statistics Made Simple (2012)
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Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#32For me the best so far book on Bayesian probability was "Probability Theory: The Logic of Science: Principles and Elementary Applications" by E. T. Jaynes. The 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 add…
Great book. Unfortunately Jaynes passed away before he could finish it. Still, a great take on probability theory.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#33Earlier quoted context omitted.
I'm not sure why you were down-voted. This is a valid point and as a college professor and author, I'm sure Downey would appreciate any feedback that would make his book better.
It looks like someone is overly sensitive to criticism. Instead of being a coward and voting valid comments down without comment, why don't you add to the discussion by stating why you think the comment was wrong/invalid/etc., thus enlightening the rest of us with your superiority?
I can't downvote. The author points out his differences and provides his reasons why. The criticism doesn't add any value to the conversation because it has been addressed already by the author himself.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#34I was surprised to learn about the 'Likelihoodist', an interpretation of bayes that avoids ambiguities with choosing a prior.
An Introduction to Likelihoodist, Bayesian, and Frequentist Methods
http://gandenberger.org/2014/07/28/intro-to-statistical- methods-2/
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#35Earlier quoted context omitted.
It looks like someone is overly sensitive to criticism. Instead of being a coward and voting valid comments down without comment, why don't you add to the discussion by stating why you think the comment was wrong/invalid/etc., thus enlightening the rest of us with your superiority?
I can't downvote. The author points out his differences and provides his reasons why. The criticism doesn't add any value to the conversation because it has been addressed already by the author himself.
I disagree. In Python, the PEP8 standard is to use snake case for variables and function names. Classes should appear in this format: ClassName. Downey uses the class style for functions because, according to him, he feels it would be too inconvenient to do it the right way. This is a lazy cop-out. If you're writing a book targeting the Python community you should adhere to the Python PEP8 standards out of respect for your readers, if nothing else. The parent poster poses a valid question and it most definitely adds to the conversation because it calls into question the author's respect for his audience.
How is what Downey did any different from me writing a book and stating that correct spelling, grammar, and editing would be too inconvenient for me, so I'm just going to type whatever I feel like and that should be OK, because I addressed my lack of quality and attention to detail during my introduction?
By the way. Thanks for commenting.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#36My 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
> He memorized pictures and bios of all 90 students in the first year class at Olin College of Engineering.
Do you know if he was using spaced repetition to do that? I know some teachers have tried that to speed up learning their students.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#37My 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
>He memorized pictures and bios of all 90 students in the first year class at Olin College of Engineering.
It's impressive not so much that he did that, but that he bothered to try.
Most lecturers (myself included) will try very hard not to learn anything about their students because they consider actually dealing with undergrads (particularly first-years!) on an individual level is beneath them.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#38For 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
You write that the frequentist doesn't answer the question, but it does. It answers P(H') = (H/H+T)^H' You also write that the frequentist solution fails to give an error estimate, yet you don't show that the Bayesian solution does give one. If the goal of the article is to show that Bayesian is more correct than frequentist then it leaves the reader unconvinced. If the goal is to show 3 ways of finding a probability…
> You write that the frequentist doesn't answer the question, but it does. It answers: P(H') = (H/H+T)^H'
The question was asking for P(H' | H, T), not P(H').
> You also write that the frequentist solution fails to give an error estimate, yet you don't show that the Bayesian solution does give one.
Because there is no error? In the proof I assume P(p) is known and then after that every step follows from a law of probability. There is no error to be accounted for in the procedure. The only caveat is that we need to know P(p) to be able to perform the procedure, which is a caveat that I point out at least 3 times in the page.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#39It should also have one called "Think Markov Chain Monte Carlo" - even the simplest reference is intractable and others began very simplistically and ends incomprehensible enough to disgust the subject altogether.
Re: Think Bayes: Bayesian Statistics Made Simple (2012)
#40For 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
You write that the frequentist doesn't answer the question, but it does. It answers P(H') = (H/H+T)^H' You also write that the frequentist solution fails to give an error estimate, yet you don't show that the Bayesian solution does give one. If the goal of the article is to show that Bayesian is more correct than frequentist then it leaves the reader unconvinced. If the goal is to show 3 ways of finding a probability…
> You write that the frequentist doesn't answer the question, but it does. It answers
> P(H') = (H/H+T)^H'
That's not the probability of getting H' heads in a row. It's an estimate of the probability of getting H' heads in a row based on a Maximum Likelihood estimation.
It doesn't make much sense if you take it to be the probability of getting H' heads in a row. For example, if {H=1, T=0}, then P(H'=100) = 1. You looked at one flip, and then decided that every subsequent flip was guaranteed to be heads?
It becomes even more clear that the question isn't really being answered if you take {H=0, T=0}.