It would be good for some readers, but could be problematic for instructors... what if they want to use some problems for HW and their students know this link... what about for your course?

I polled instructors previously, and nearly all had no objections. In fact, some welcomed the release of the solutions. For instructors who want to use the exercises for grading students, it is easy to change the numbers (data) so that students still have to execute the method and produce their own results.

Thanks for doing this. It's a real boon for professionals like me who were raised on the classical method and are trying to bootstrap our way forward into the present.

Thanks so much for doing this. I'm using your book to teach myself bayesian data analysis and the solutions allow me to check that I'm on the right track.

I find the solutions very helpful as well. Would it be possible to publish the code electronically as well? I find it more helpful to walk through the code line by line in R so that I can see the execution rather than typing it in which is rather tedious.

Dear Anonymous December 16: Well, the exercises are supposed to be exercises, not just more demo's like the text. There's value in having to do the exercises, not just clicking "run" on a working demo. So, I'll keep the solutions as they are, without providing complete code for every answer. But thanks for asking, and thanks for your interest in the book!

Thank you for this! I'm using this book to learn Bayesian method on my own (I come from the frequentist school) and it's good to have solutions to double check I understood the material.

Really enjoying the book. Not sure if this is the place to post this, but, on the solutions page, problem 4.7 answer, there is a typo. The denominator equation is correct up to the final p(theta=.75) It should be .25. The probabilities should = 1 (.25+.5+.25). Your final answer of p(D)=0.0004158 is only possible if the .75 is actually .25.

Not sure if you corrected this elsewhere.

Thanks for a wonderful book!

Scott -from Cincinnati now Bay Area -sister is an IU alumna

It would be good for some readers, but could be problematic for instructors... what if they want to use some problems for HW and their students know this link... what about for your course?

ReplyDeleteI polled instructors previously, and nearly all had no objections. In fact, some welcomed the release of the solutions. For instructors who want to use the exercises for grading students, it is easy to change the numbers (data) so that students still have to execute the method and produce their own results.

ReplyDeleteThanks for doing this. It's a real boon for professionals like me who were raised on the classical method and are trying to bootstrap our way forward into the present.

ReplyDeleteThanks so much for doing this. I'm using your book to teach myself bayesian data analysis and the solutions allow me to check that I'm on the right track.

ReplyDeleteI am learning Baysesian data analysis on my own and having the solution to check my understanding has been very helpful. Thank you.

ReplyDeleteI find the solutions very helpful as well. Would it be possible to publish the code electronically as well? I find it more helpful to walk through the code line by line in R so that I can see the execution rather than typing it in which is rather tedious.

ReplyDeleteDear Anonymous December 16:

ReplyDeleteWell, the exercises are supposed to be exercises, not just more demo's like the text. There's value in having to do the exercises, not just clicking "run" on a working demo. So, I'll keep the solutions as they are, without providing complete code for every answer. But thanks for asking, and thanks for your interest in the book!

Thank you for this! I'm using this book to learn Bayesian method on my own (I come from the frequentist school) and it's good to have solutions to double check I understood the material.

ReplyDeleteHello,

ReplyDeleteReally enjoying the book.

Not sure if this is the place to post this, but, on the solutions page, problem 4.7 answer, there is a typo. The denominator equation is correct up to the final p(theta=.75)

It should be .25. The probabilities should = 1 (.25+.5+.25). Your final answer of p(D)=0.0004158 is only possible if the .75 is actually .25.

Not sure if you corrected this elsewhere.

Thanks for a wonderful book!

Scott

-from Cincinnati now Bay Area

-sister is an IU alumna

Thank you so much ! You rock - this has been one of the best books on Bayesian Analysis. I am sure Thomas Bayes would be proud of you ! :D

ReplyDelete