Hacker News Reader

Comment by rwilson4 | Hacker News Reader

Bayesian methods have a lot going for them, but you need some way of checking whether the methods are giving sensible answers. Andrew Gelman (well know professor, blogger, and author of Bayesian Data Analysis) relies on simulation, which is a pretty Frequentist thing to do!

Bayesian and Frequentist methods are not nearly as at odds as posts like this suggest. Frequentism is mostly about how methods should be evaluated. Bayesianism is mostly about how to incorporate different sources of information. You can assess the Frequentist properties of Bayesian methods!

Larry Wasserman wrote a great post about this topic here: https://normaldeviate.wordpress.com/2012/11/17/what-is-bayes...

View on HN

Almost all modern Bayesian models rely heavily on simulation. This is mostly because it's a very effective way to do numerical integration, and almost all non-trivial Bayesian models involve integrals without closed form solutions.

This is true but the poster above doesn’t mean Monte Carlo integration. Rather, testing those methods on simulations from generative models.

I also don't like when it's framed as an "either-or" dichotomy.

It's like a programming language - there is no "best" one in general, you just have to learn how to choose the best tool for the job at hand, understanding each tool's strengths and weaknesses. It's not that mysterious.

it may not be a personally acceptable, but there is a real world divide where a large subset of people pretty much only accept frequentism. This is often the same group publishing under powered, low n, studies.

The biggest issue with frequentism is the assumptions. I can't rattle them off like I used to be able to, but almost every real world scenario where statistics are useful are going to violate some of them, and yet frequentists will simply carry on.

It's really a cultural issue around 'correctness', and frequentism is often reduced to an appeal to authority.

While I agree, the most important benefits of bayesian methods - in my opinion - are forcing people to consider (1) uncertainty and (2) effect sizes. These two things alone are a fundamental difference and a major step forward.

At the risk of a no-true-Scotsman argument, I want to point out the difference between Frequentism as practiced by statisticians and non-statisticians. Non-statisticians will simply say that a result was stat sig, without specifying even the corresponding p-value, let alone an effect size point estimate or confidence interval.

I always report confidence intervals front and center, and bring in point estimates and p values as supporting characters. And of course discussing to what extent the study design supports causal conclusions.

Reply on news.ycombinator.com