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Show HN: My Book Making Probability So Easy That a Lemur Could Learn It

14 points by wwwpatdelcom 5y ago

Probability involves a lot of extra-mathematical concepts and funny transforms. I have always had a hard time internalizing it. When I heard a couple years ago that Lemurs are actually capable of abstract math, that gave me a bit of motivation to try to finally conceptualize probability in terms of whole numbers, counts, geometric shapes, basically easy-to-remember mathematical primitives. This book might be interesting to folks interested in predictive science, research, or just anyone who may have taken probability or statistics and might like a refresher on some of the edges of these sometimes tough to nail down concepts. I would love any feedback or questions that anyone may have. Thanks! Patrick Delaney

wwwpatdelcom 5y ago
I see a ton of people have been using the free code I submitted on hackernews and one person using a Twitter code since yesterday even opted to buy a copy! Hey awesome, thanks! It would be really great to get people's feedback, but you don't have to comment here - I realize it may take some time to digest since it's 200+ pages. Remember if you subscribe you can just send me an email at any point if you come across an error or something that wasn't quite clear, or just any other idea of how I could improve. thanks!
ad404b8a372f2b9 5y ago
I wrote a condensed primer on probabilities for a few struggling students years ago so I was curious to check this one out. The concept is interesting but I think the execution needs some extra work. In parts I felt like the lemur abstraction obfuscated things rather than elucidating them. I have a lot of criticism, I hope you won't be discouraged by it. You mix concepts from probability and statistics, I feel it would gain from sticking with either or separating them into different parts/books. Crucial probability concepts are relegated to the end (conditional probabilities, Baye's theorem, ...). The plan in general has some issues, I was surprised to see the expected value as one of the last chapters, and you actually reference it dozens of time before it's introduced. For example slide 15 which would mean nothing to someone who'd just started learning about probabilities. I mostly skipped around but took some time to read carefully the chapters on random variables and PDFs because they're usually the hardest topics to grasp for a beginner (mostly because of the confusing notations). The "random variable" chapter doesn't introduce what a random variable is, it's about dependent and independent variables which are more relevant to the topic of statistical modeling. The PDF chapter is alright but I felt like it was stretching the limits of the lemur abstraction and in the end gave up on it entirely when you start talking about curves and area under the curve. Also, a couple of nitpicks purely about the form, but I don't understand why you refer to probability as "chance" throughout this chapter, and your numbers use too many decimals, you have the luxury of choosing your numbers you can use nice round ones expressed as percentages. So to conclude, my advice would be: 1) Rework the plan 2) Drop the statistics 3) Restrain yourself to discrete probabilities. It's enough for the student to get an intuition of all important concepts and it will be much easier to explain. For example the chapter on PDFs could have been on PMFs and it would have been much easier to explain without compromising your abstraction.
- wwwpatdelcom 5y ago
  Greetings and thank you for your feedback. I would love to take a gander at your primer if you still have that handy. I don't feel discouraged at all and I appreciate your feedback and thank you wholeheartedly for it. First off, I have been told already on another platform that I did actually refer to Expected Value in the book prior to introducing it - so yes, that is absolutely in the change order for the next version on Tau Day. I was in a rush to publish this last week for Pi Day. I had reorganized the chapters at one point and moved Expected Value way to the back because I saw it more in line with making a prediction rather than it being a parameter. Originally I had this book organized like a traditional textbook, but one of the points I would like to drive through is the, "workflow" of using probability to make a prediction, meaning, you 1) Calculate Parameters then 2) Find relationships between Sets and finally 3) Then and only then, make predictions, which involve a value and a relationship statement. Part of this is because there is such a huge ongoing trend in wanting to make predictions, gain more certainty over the future, work higher end jobs leveraging statistical computing and machine learning, etc. - so I'm thinking there are a lot of folks out there who want probability described from yet another standpoint. That being said, if I have just repeated what has already been said, then I have failed. On the bright side, with LeanPub I can just clean it up and release a new version of the book. In most textbooks, Expected Value is taught way at the beginning, I think because it's, "easier" and perhaps there isn't a lot of thought put into the workflow I have described above. What it sounds like I should do is comb through and remove references to Expected Value earlier on in the book. I could also certainly clean up any technical terms like that as well...not sure if you had any other ones you identified? Yes you are right about stretching the lemur abstraction. To a certain degree, we really can't teach lemurs any of this stuff, it's more of a motif for making things visual for humans, not lemurs. On the other hand, I do offer somewhat of a rebuttal to the idea that, "lemurs could never learn X," in this YouTube video I produced a few months ago on Bayesian Statistics. As to your criticism about statistics vs. probability - to be honest that one confuses me a bit. What are you referring to specifically? What do you see as being statistics and what do you see as probability and where do you draw the line?

14 points by wwwpatdelcom 5y ago

wwwpatdelcom 5y ago

I see a ton of people have been using the free code I submitted on hackernews and one person using a Twitter code since yesterday even opted to buy a copy! Hey awesome, thanks! It would be really great to get people's feedback, but you don't have to comment here - I realize it may take some time to digest since it's 200+ pages. Remember if you subscribe you can just send me an email at any point if you come across an error or something that wasn't quite clear, or just any other idea of how I could improve. thanks!

ad404b8a372f2b9 5y ago

I wrote a condensed primer on probabilities for a few struggling students years ago so I was curious to check this one out. The concept is interesting but I think the execution needs some extra work. In parts I felt like the lemur abstraction obfuscated things rather than elucidating them. I have a lot of criticism, I hope you won't be discouraged by it. You mix concepts from probability and statistics, I feel it would gain from sticking with either or separating them into different parts/books. Crucial probability concepts are relegated to the end (conditional probabilities, Baye's theorem, ...). The plan in general has some issues, I was surprised to see the expected value as one of the last chapters, and you actually reference it dozens of time before it's introduced. For example slide 15 which would mean nothing to someone who'd just started learning about probabilities. I mostly skipped around but took some time to read carefully the chapters on random variables and PDFs because they're usually the hardest topics to grasp for a beginner (mostly because of the confusing notations). The "random variable" chapter doesn't introduce what a random variable is, it's about dependent and independent variables which are more relevant to the topic of statistical modeling. The PDF chapter is alright but I felt like it was stretching the limits of the lemur abstraction and in the end gave up on it entirely when you start talking about curves and area under the curve. Also, a couple of nitpicks purely about the form, but I don't understand why you refer to probability as "chance" throughout this chapter, and your numbers use too many decimals, you have the luxury of choosing your numbers you can use nice round ones expressed as percentages. So to conclude, my advice would be: 1) Rework the plan 2) Drop the statistics 3) Restrain yourself to discrete probabilities. It's enough for the student to get an intuition of all important concepts and it will be much easier to explain. For example the chapter on PDFs could have been on PMFs and it would have been much easier to explain without compromising your abstraction.

wwwpatdelcom 5y ago
Greetings and thank you for your feedback. I would love to take a gander at your primer if you still have that handy. I don't feel discouraged at all and I appreciate your feedback and thank you wholeheartedly for it. First off, I have been told already on another platform that I did actually refer to Expected Value in the book prior to introducing it - so yes, that is absolutely in the change order for the next version on Tau Day. I was in a rush to publish this last week for Pi Day. I had reorganized the chapters at one point and moved Expected Value way to the back because I saw it more in line with making a prediction rather than it being a parameter. Originally I had this book organized like a traditional textbook, but one of the points I would like to drive through is the, "workflow" of using probability to make a prediction, meaning, you 1) Calculate Parameters then 2) Find relationships between Sets and finally 3) Then and only then, make predictions, which involve a value and a relationship statement. Part of this is because there is such a huge ongoing trend in wanting to make predictions, gain more certainty over the future, work higher end jobs leveraging statistical computing and machine learning, etc. - so I'm thinking there are a lot of folks out there who want probability described from yet another standpoint. That being said, if I have just repeated what has already been said, then I have failed. On the bright side, with LeanPub I can just clean it up and release a new version of the book. In most textbooks, Expected Value is taught way at the beginning, I think because it's, "easier" and perhaps there isn't a lot of thought put into the workflow I have described above. What it sounds like I should do is comb through and remove references to Expected Value earlier on in the book. I could also certainly clean up any technical terms like that as well...not sure if you had any other ones you identified? Yes you are right about stretching the lemur abstraction. To a certain degree, we really can't teach lemurs any of this stuff, it's more of a motif for making things visual for humans, not lemurs. On the other hand, I do offer somewhat of a rebuttal to the idea that, "lemurs could never learn X," in this YouTube video I produced a few months ago on Bayesian Statistics. As to your criticism about statistics vs. probability - to be honest that one confuses me a bit. What are you referring to specifically? What do you see as being statistics and what do you see as probability and where do you draw the line?