This is the first book on statistics I read from cover to cover! :-)
I have serious doubts it can serve as a textbook, and I would be really surprised if someone without any knowledge of statistics could really learn it from this book. Gonick&Smith‘s book is a great source of entertainment for those who love cartoons – the cartoonist is the first author for a good reason – and love statistics, and, therefore, know it already at some level. I would definitely recommend it to both beginners (as a complementary text for keeping motivation) and to professionals (as, for instance, a perfect in-flight reading).
There is just one tiny thing I am not comfortable with: throughout the book authors maintain the idea that it is ok to be afraid of mathematics. I completely understand why they do this, yet I feel this is a wrong message. To me, to be afraid of mathematics is the same as to be afraid of dogs. Yes, indeed, a dog bite in childhood may develop a life-long phobia of dogs. Similarly, a bad first math teacher may cause a “math allergy” for the rest of life. However, it is not good to be scared of either. Both mathematics and dogs are our friends. Big friends, indeed!
As I have already mentioned, I was invited to give a talk at the Southern California Probability Symposium 2001. After some thinking, I have decided to talk about the MCMC revolution that has happened over the last decade in the field of reliability engineering that lies at the boundary of engineering sciences and applied probability. This talk will be in the spirit of this paper by Persi Diaconis of Stanford.
Southern California Probability Symposiums have a reach history.
This is the schedule for SCPS-2011:
It is a great pleasure and honor for me to be in this company!
The arXiv is a free scientific repository hosted by Cornell University, where physicists, mathematicians, and statisticians post their preprints. It was estimated that (as of September 2011) the repository had accumulated approximately 700,000 papers, with more than 6,000 new submissions arriving each month.
I have been using arXiv for a long time already, but, by some mysterious reason (a mixture of laziness and modesty), I did not put my own papers on arXiv. A couple of days ago I did my first bit by uploading my last paper co-authored with J.L. Beck of Caltech. In this paper, we introduce a new scheme for Bayesian inference which is based on Markov chain Monte Carlo, importance sampling, and simulated annealing.