Can anyone recommend a 'Bayesian statistics the hard way' book?
Probability Theory: The Logic of Science by Edwin Jaynes
Interview on ”Bayesian Statistics the Fun Way”
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Re: Interview on ”Bayesian Statistics the Fun Way”
#12Can anyone recommend a 'Bayesian statistics the hard way' book?
Bayesian Data Analysis by Andrew Gelman
Re: Interview on ”Bayesian Statistics the Fun Way”
#13As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…
I agree with this approach, and this is roughly the approach my own Statistics master's degree takes as well. It can be challenging to understand the finer points of likelihoods and posteriors (and the how to choose a prior) without serious mathematics that you're unlikely to have upon entering a graduate statistics degree.
Starting with applied probability and applied statistics (incl. regression, ANOVA, GLMs) allow you to solve problems and feel useful and engaged before being thrown into the mathematical rigor required of Bayesian statistics.
Re: Interview on ”Bayesian Statistics the Fun Way”
#14>First of all, p-values are not the way sane people answer questions
I think they are pretty close to the way sane people answer some kinds of questions.
Re: Interview on ”Bayesian Statistics the Fun Way”
#15Can anyone recommend a 'Bayesian statistics the hard way' book?
Statistical Rethinking: A Bayesian Course with Examples in R and Stan is also considered pretty good.
Re: Interview on ”Bayesian Statistics the Fun Way”
#16As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…
I agree, although I respect those who look for deeper justification for the methods we use. Bayesian statistics/decision theory does have axiomatic foundations after all.
Re: Interview on ”Bayesian Statistics the Fun Way”
#17Can anyone recommend a 'Bayesian statistics the hard way' book?
Bayesian Data Analysis by Andrew Gelman http://www.stat.columbia.edu/~gelman/book/
one vote for BDA. For programmers who learn better by implementing things, this book [1] is also good:
[1]: https://www.amazon.com/Bayesian-Methods-Hackers-Probabilisti...
Re: Interview on ”Bayesian Statistics the Fun Way”
#18"For coin tosses both schools of thought work pretty well"
How many coin tosses in a row have to land heads before a frequentist decides that the coin is unfair?
Re: Interview on ”Bayesian Statistics the Fun Way”
#19As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a maj…
Respectfully, I find that people who are not statisticians overwhelming disagree with your point on which should be taught first.
Bayesian is a natural order of inference for people. The whole concept of the black swan ("all swans are white") proves this out.
Frequentist statistics is much less intuitive to people.
My preference is for people to be able to use some statistics, and Bayesian gets them productive faster.
Re: Interview on ”Bayesian Statistics the Fun Way”
#20As someone who uses statistics all the time at work, I sympathize so much with this article and greatly enjoyed it. Every time I try to introduce a Bayesian prior, coworkers either look at me like I'm crazy (because they've never heard of or used Bayesian stats) or like I've suddenly gone soft and introduced a bunch of nebulous, touchy-feely context into the objective truth (if they're dedicated frequentists). Then w…
> like I've suddenly gone soft and introduced a bunch of nebulous, touchy-feely context into the objective truth This drives me nuts. If you haven't, check out the paper "Beyond subjective and objective in statistics" by Gelman and Hennig (2017). Right at the beginning they make the point that any analysis includes external information in many ways, such as adjusting variables for imbalance, how we deal with outliers…
> The idea that priors are somehow ruining an "objective" model is just absurd to me.
I think some caution can be justified to a certain extent (not the blind "emotional" objections). When establishing priors in a low data regime, one must necessarily be careful. It's a knob whose mass can change a lot in the inference conclusion. That said, if we trust our belief about the region the available data do not inform us well of, why not utilize our domain knowledge/belief?