To get a more introductory—but still quite thorough, and more modern—Bayesian perspective, I recommend John Kruschke’s Doing Bayesian Data Analysis. Ignore the silly cover. The book is engagingly written and informative. As a side benefit, it will also teach you R, a very useful language for statistical computing. Definitely worth learning if you are at all interested in data analysis.
Also, you should learn some classical statistics before getting into Bayesian statistics. Jaynes won’t really help with that. Kruschke will help a little, but not much. The freely available OpenIntro Statistics textbook is a very good introduction.
I recommend first reading OpenIntro, then Kruschke, then Jaynes.
Also, you should learn some classical statistics before getting into Bayesian statistics.
I’m of two minds about that. I did classical first, and found it painful. It was just wrong. Recipes without real justification. Jaynes was such a relief after that. He just made sense, step after step.
So I would have wished to have started with Jaynes.
But maybe it’s good to learn the horrible way first, so that you really appreciate the right way?
Nah, that seems rather demented. Learn the right way first. Learn Jaynes. He covers the basic classical statistical methods anyway, and in a better fashion than classical statistics classes do. He just makes more sense.
This sounds more like a pedagogical issue than an inherent problem with classical statistics. I agree that Bayesianism is philosophically “cleaner”, but classical statistics isn’t just a hodge-podge of unjustified tools. If you’re interested in a sophisticated justification of classical methods, this is a good place to start. I’m pretty sure you’ll be unconvinced, but it should at least give you some idea of where frequentists are coming from.
“Recipes without real justification. Jaynes was such a relief after that. He just made sense, step after step.”
I am not a “classical statistician”, but Harald Cramer’s ’http://www.amazon.com/Mathematical-Methods-Statistics-Harald-Cram%C3%A9r/dp/0691005478′ is still incredibly relevant. He is also famous for relevant results in insurance mathematics and risk theory. It wouldn’t be too much of a understatement to say he is the father of modern ruin theory. Something that should otherwise be relevant to all people who care about tail risk.
Do you mean classical, as in the classic frequency of Cramers? Cramers view is still essential. What about logical frequency views such as Kyburg’s? Is that ‘classical’? Is the difference between the logical approach of Jaynes and the Logical Frequentist approach of Kyburg’s closer than Jaynes vs other Bayesians?
Jaynes is a top tier book, but it is false to say that it covers classical statistics better than Harald Cramer’s.
I don’t think buybuydandavis was saying that Jaynes covers classical statistics well, but that classical statistics isn’t worth covering well and that Jaynes covers more useful things well.
For an introductory course on statistics (which uses the OpenIntro Statistics textbook), I strongly recommend Coursera’s Data Analysis and Statistical Inference. Before I found this course, I tried Coursera’s Statistics One and Udacity’s Intro to Statistics, neither of which I recommend.
I agree with the Kruschke recommendation. I bought a copy of Doing Bayesian Data Analysis a couple of weeks ago and am working my way through it now. It is quite good. You’ll need an understanding of undergraduate-level calculus and some background in basic probability to understand it, I think.
To get a more introductory—but still quite thorough, and more modern—Bayesian perspective, I recommend John Kruschke’s Doing Bayesian Data Analysis. Ignore the silly cover. The book is engagingly written and informative. As a side benefit, it will also teach you R, a very useful language for statistical computing. Definitely worth learning if you are at all interested in data analysis.
Also, you should learn some classical statistics before getting into Bayesian statistics. Jaynes won’t really help with that. Kruschke will help a little, but not much. The freely available OpenIntro Statistics textbook is a very good introduction.
I recommend first reading OpenIntro, then Kruschke, then Jaynes.
I’m of two minds about that. I did classical first, and found it painful. It was just wrong. Recipes without real justification. Jaynes was such a relief after that. He just made sense, step after step.
So I would have wished to have started with Jaynes.
But maybe it’s good to learn the horrible way first, so that you really appreciate the right way?
Nah, that seems rather demented. Learn the right way first. Learn Jaynes. He covers the basic classical statistical methods anyway, and in a better fashion than classical statistics classes do. He just makes more sense.
This sounds more like a pedagogical issue than an inherent problem with classical statistics. I agree that Bayesianism is philosophically “cleaner”, but classical statistics isn’t just a hodge-podge of unjustified tools. If you’re interested in a sophisticated justification of classical methods, this is a good place to start. I’m pretty sure you’ll be unconvinced, but it should at least give you some idea of where frequentists are coming from.
Please do not make statements like
“Recipes without real justification. Jaynes was such a relief after that. He just made sense, step after step.”
I am not a “classical statistician”, but Harald Cramer’s ’http://www.amazon.com/Mathematical-Methods-Statistics-Harald-Cram%C3%A9r/dp/0691005478′ is still incredibly relevant. He is also famous for relevant results in insurance mathematics and risk theory. It wouldn’t be too much of a understatement to say he is the father of modern ruin theory. Something that should otherwise be relevant to all people who care about tail risk.
Do you mean classical, as in the classic frequency of Cramers? Cramers view is still essential. What about logical frequency views such as Kyburg’s? Is that ‘classical’? Is the difference between the logical approach of Jaynes and the Logical Frequentist approach of Kyburg’s closer than Jaynes vs other Bayesians?
Jaynes is a top tier book, but it is false to say that it covers classical statistics better than Harald Cramer’s.
I don’t think buybuydandavis was saying that Jaynes covers classical statistics well, but that classical statistics isn’t worth covering well and that Jaynes covers more useful things well.
For an introductory course on statistics (which uses the OpenIntro Statistics textbook), I strongly recommend Coursera’s Data Analysis and Statistical Inference. Before I found this course, I tried Coursera’s Statistics One and Udacity’s Intro to Statistics, neither of which I recommend.
I agree with the Kruschke recommendation. I bought a copy of Doing Bayesian Data Analysis a couple of weeks ago and am working my way through it now. It is quite good. You’ll need an understanding of undergraduate-level calculus and some background in basic probability to understand it, I think.