# Tag Info

9

Peter D. Huff. A First Course in Bayesian Statistical Methods. Springer (2010) Also Andrew Gelman et. al. Bayesian Data Analysis (3rd ed.). CRC (2013) The Gelman book isn't constrained to R but also uses Stan, a probabilistic programming language similar to BUGS or JAGS. I believe earlier editions of the book used BUGS instead of Stan, which is ...

5

Fortuitous timing, as Bayesian Data Analysis, 3rd ed was just released. It's a good general-purpose text, with an emphasis on hierarchical methods, a section on advanced computation (that is, Markov chain Monte Carlo), and an appendix on Gelman's Bayesian inference tool, rstan. The text focuses on statistics rather than programming, though, so perhaps ...

4

I recommend that you be explicit about all elements of the plot. Explain how the boxplot indicates the median (mean?), quartiles (quantiles?), and extreme values (distant quantiles?)... assuming that's what you're plotting. I suggest you be explicit here not only for clarity, but also because the general boxplot template can be used to display different ...

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If you are fitting a multivariate regression tree (MRT), then you need method = "mrt" - i.e. if you are using a matrix for the response, you can't use method = "class". If you just have a vector response, then if this is a factor and you use method = "class", then mvpart is doing nothing different to rpart i.e. the usual thing for a classification tree. If ...

2

The fact that you are using 4 out of 14 parameters implied that you used significance testing to select the variables. This is invalid. There are a number of other problems: Your total sample size is far too small for data splitting to be a reliable method You are seeking arbitrary cutoffs You are not using a proper accuracy score such as deviance, ...

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There is "The Handbook of the Normal Distribution" by J.K. Patel & C.B. Read (1982) which is a focused reference for the Normal distribution. The book mainly focus on the univariate case, but has a chapter dedicated to the bivariate case. For the multivariate case, Y. L. Tong (1990), "The Multivariate Normal Distribution".

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genetic algorithms were used to lower the prime gap to 4680 in the recent Zhang twin primes proof breakthrough & associated polymath project. the bound has been lowered by other methods but it shows some potential for machine-learning approaches in this or related areas. they can be used to devise/optimize effective "combs" or basically sieves for ...

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I guess I would like to read or at least browse in that too, but only a polymath or a committee could write it, and the polymath isn't evident and committee books often don't work well. Also, many of the general books on statistics that tend to pop up from (e.g.) searches on Amazon just leave out most of the interesting technical details and/or are written ...

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