Can You Self-Learn Bayesian Statistics and R Programming?

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Self-learning Bayesian statistics and R programming is feasible, especially with the right resources. Recommended materials include "Data Analysis and Graphics Using R" by John Maindonald and John Braun, although it may be slightly outdated. For Bayesian inference, there are high-quality study notes available in PDF format that can be shared. It's important to approach Bayesian statistics critically, recognizing it as a framework rather than an absolute truth. Utilizing online tutorials and community resources can further enhance the self-learning experience.
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There isn't a course at my university for Bayesian stats, but I was wondering if it would be possible to teach myself. Does anyone know of any good intro texts or online resources for leaning Bayesian Statistics? Also I want to learn to use R some over the break. Does anyone know of any good tutorials or learning resources for R?

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I have the book Data Analysis and Graphics Using R by John Maindonald and John Braun, and it's pretty good, although it might be a tad outdated now, I'm not sure. It's published by the Cambridge University Press.

As far as Bayesian inference, I'm not sure about books, however for one of my theory classes there were top-notch book-format study notes that I have in pdf format and can email to you. They're quite good.
 
It is good to learn Bayesian Stat, but do not believe it is the truth.
 
I was reading documentation about the soundness and completeness of logic formal systems. Consider the following $$\vdash_S \phi$$ where ##S## is the proof-system making part the formal system and ##\phi## is a wff (well formed formula) of the formal language. Note the blank on left of the turnstile symbol ##\vdash_S##, as far as I can tell it actually represents the empty set. So what does it mean ? I guess it actually means ##\phi## is a theorem of the formal system, i.e. there is a...

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