Seeing Theory: A visual introduction to probability and statistics (2017)
seeing-theory.brown.edu
Seeing Theory: A visual introduction to probability and statistics (2017)
1–10 of 21 posts
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#2Glad to see this once more.
Some Hacker News Discussions of this:
1. https://news.ycombinator.com/item?id=13735714
2. https://news.ycombinator.com/item?id=13760353
I wish they'd cover the sample space/parameter space distinction "harder", as it seems key to the numerous philosophical divides in the foundations of statistics & probability theory, and it seems like a very good candidate for colorful & animated visualization.
Also, note that the classic coin flipping example used there right in the beginning serves as an extremely bad & misleading analogy, see here for why:
https://econ.ucsb.edu/~doug/240a/Coin%20Flip.htm
I wish we'd stop using it in Stats & Physics 101, or at least add huge disclaimers to it, something like "Coins don't actually behave this way, not even mathematically idealized ones".
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#3I looked up the Victor Powell visualization they cite and it's quite a bit more in-depth then the version they have:
But seriously, all of these interactive visualizations are amazing. I wish they had this kind of stuff when I was an undergrad. I remember trying to plot the beta distribution for different alpha and beta parameters in MATLAB trying to get an intuition for it; just dragging the sliders around on the prior and watching it update as each data point comes in is a million times more accessible.
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#4Glad to see this once more. Some Hacker News Discussions of this: 1. https://news.ycombinator.com/item?id=13735714 2. https://news.ycombinator.com/item?id=13760353 I wish they'd cover the sample space/parameter space distinction "harder", as it seems key to the numerous philosophical divides in the foundations of statistics & probability theory, and it seems like a very good candidate for colorful & animated visualiz…
I saw Persi Diaconis lecture on coin flips and shuffling cards once. Very interesting guy.
What metaphor for a Bernoulli trial do you prefer?
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#5They are working on a book too:
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#6Glad to see this once more. Some Hacker News Discussions of this: 1. https://news.ycombinator.com/item?id=13735714 2. https://news.ycombinator.com/item?id=13760353 I wish they'd cover the sample space/parameter space distinction "harder", as it seems key to the numerous philosophical divides in the foundations of statistics & probability theory, and it seems like a very good candidate for colorful & animated visualiz…
I saw Persi Diaconis lecture on coin flips and shuffling cards once. Very interesting guy. What metaphor for a Bernoulli trial do you prefer?
Lecture 16 in this Discrete Probability pdf[1] covers how Diaconis and his students decrypted some prison ciphertext brought to them
[1] http://www.cs.cmu.edu/~odonnell/papers/probability-and-compu...
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#7Earlier quoted context omitted.
I saw Persi Diaconis lecture on coin flips and shuffling cards once. Very interesting guy. What metaphor for a Bernoulli trial do you prefer?
Lecture 16 in this Discrete Probability pdf[1] covers how Diaconis and his students decrypted some prison ciphertext brought to them [1] http://www.cs.cmu.edu/~odonnell/papers/probability-and-compu...
I found the original account by Diaconis, with more detail:
"The Markov Chain Monte Carlo Revolution" (it's the first example in the paper)
https://math.uchicago.edu/~shmuel/Network-course-readings/MC...
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#8Glad to see this once more. Some Hacker News Discussions of this: 1. https://news.ycombinator.com/item?id=13735714 2. https://news.ycombinator.com/item?id=13760353 I wish they'd cover the sample space/parameter space distinction "harder", as it seems key to the numerous philosophical divides in the foundations of statistics & probability theory, and it seems like a very good candidate for colorful & animated visualiz…
Your ucsb link describes an unfair coin flipping protocol, and not the fair protocol commonly used in practice. In particular, it provides a "strategy" at the end to generate an unfair toss (which requires, e.g., that you be both the flipper and the chooser). Other studies, such as the famous You can load a die, bit you can't bias a coin investigate fair flipping protocols and argue that you cannot, in fact, bias a coin:
https://www.stat.columbia.edu/~gelman/research/published/dic...
I have some blog posts on developing biased coins and dice, and the results show that any coin with a measurable bias is quite obviously not fair:
https://izbicki.me/blog/how-to-create-an-unfair-coin-and-pro...
whereas you can undetectably bias dice at home with just a bit of water:
https://izbicki.me/blog/how-to-cheat-at-settlers-of-catan-by...
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#9Love to see people trying to make topics in math and cs more accessible to a wider audience
Re: Seeing Theory: A visual introduction to probability and statistics (2017)
#10I looked up the Victor Powell visualization they cite and it's quite a bit more in-depth then the version they have: http://setosa.io/conditional/ But seriously, all of these interactive visualizations are amazing. I wish they had this kind of stuff when I was an undergrad. I remember trying to plot the beta distribution for different alpha and beta parameters in MATLAB trying to get an intuition for it; just draggin…
Sometimes a good analogy can take the place of a visualization [1], albeit I did imagine a distribution getting more concentrated.
[1] http://varianceexplained.org/statistics/beta_distribution_an...