Any motivated undergrad in a technical field has enough background. If you want to read other folks in the field, they tend to be more statistical than Pearl (and thus in addition it’s good to have basic stat). It helps to have taken at least one class with proofs in it.
As in, you’ve read the lecture and are now interested in tackling the book, or you’re just trying to get a hold on what the book requires?
If you understand probability calculus and have used mathematical graphs before, I think you could make it with sufficient dedication and patience. Familiarity with statistics will make much of the book more meaningful. The concepts in this book are mostly orthogonal to other mathematical formalisms (like standard statistical ones), but are in many ways more intuitive than those formalisms, because people like thinking in terms of cause and effect. (The slogan “correlation doesn’t imply causation!” exists because people want correlation to imply causation.)
I think a class on machine learning or Bayesian networks may be a gentler introduction to several of the core concepts- but the benefit of the formal approach taken by this book is that it’s very good at dissolving confusions, which more practical classes may not focus on as much.
Sorry, my orignal comment was worded poorly. I don’t have to read the lecture to know I’m interested in reading the whole book. I’m just trying to figure out how I can go through the book without getting frustrated and confused. I suppose I’ll just try to read the book and ask questions and look stuff up when I need to. It’ll probably be less enjoyable, but more useful to get used to thinking that way.
I took AP stats in high school which was pretty boring and repetitive except for when we talked about Simpson’s paradox briefly.
I think this course on Coursera would be good to take concurrently if anybody else is interested: (https://www.coursera.org/course/pgm). It’s taught by the founder of Coursera and uses her textbook (which has been recommended before). In fact, if anybody’s interested, I’d love to take this class with other LWers.
Vaniver’s post finally convinced me to get off my ass and take a look at the book—mainly because of the mention of the controversy with econometricians (I’m a Stat/Econ joint Ph.D. student… so it caught my eye). I’ve glanced through the first few chapters of the first edition (the digitial copy linked above) and through chapter 5 of the second edition (yay libraries!).
I’d recommend at least having taken a course in probability & mathematical statistics—this course might be called different things in different places, but it’s essentially a course in probability theory that uses some multivariate calculus, and might go on to a more rigorous introduction to statistics (that might be a second semester course). I’m not sure how much of the calculus is necessary for Pearl, but the more rigorous treatment of probability theory will be helpful in addition to being accustomed to a higher level of rigor in general. Unless you’re a savant of course, then you might be fine anyway.
So what are the pre-requisites for reading the book and not the lecture?
Any motivated undergrad in a technical field has enough background. If you want to read other folks in the field, they tend to be more statistical than Pearl (and thus in addition it’s good to have basic stat). It helps to have taken at least one class with proofs in it.
As in, you’ve read the lecture and are now interested in tackling the book, or you’re just trying to get a hold on what the book requires?
If you understand probability calculus and have used mathematical graphs before, I think you could make it with sufficient dedication and patience. Familiarity with statistics will make much of the book more meaningful. The concepts in this book are mostly orthogonal to other mathematical formalisms (like standard statistical ones), but are in many ways more intuitive than those formalisms, because people like thinking in terms of cause and effect. (The slogan “correlation doesn’t imply causation!” exists because people want correlation to imply causation.)
I think a class on machine learning or Bayesian networks may be a gentler introduction to several of the core concepts- but the benefit of the formal approach taken by this book is that it’s very good at dissolving confusions, which more practical classes may not focus on as much.
Sorry, my orignal comment was worded poorly. I don’t have to read the lecture to know I’m interested in reading the whole book. I’m just trying to figure out how I can go through the book without getting frustrated and confused. I suppose I’ll just try to read the book and ask questions and look stuff up when I need to. It’ll probably be less enjoyable, but more useful to get used to thinking that way.
I took AP stats in high school which was pretty boring and repetitive except for when we talked about Simpson’s paradox briefly.
I think this course on Coursera would be good to take concurrently if anybody else is interested: (https://www.coursera.org/course/pgm). It’s taught by the founder of Coursera and uses her textbook (which has been recommended before). In fact, if anybody’s interested, I’d love to take this class with other LWers.
Vaniver’s post finally convinced me to get off my ass and take a look at the book—mainly because of the mention of the controversy with econometricians (I’m a Stat/Econ joint Ph.D. student… so it caught my eye). I’ve glanced through the first few chapters of the first edition (the digitial copy linked above) and through chapter 5 of the second edition (yay libraries!).
I’d recommend at least having taken a course in probability & mathematical statistics—this course might be called different things in different places, but it’s essentially a course in probability theory that uses some multivariate calculus, and might go on to a more rigorous introduction to statistics (that might be a second semester course). I’m not sure how much of the calculus is necessary for Pearl, but the more rigorous treatment of probability theory will be helpful in addition to being accustomed to a higher level of rigor in general. Unless you’re a savant of course, then you might be fine anyway.