Empirical Bayes for multiple sample sizes
chris-said.io
Empirical Bayes for multiple sample sizes
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Re: Empirical Bayes for multiple sample sizes
#2That definition of symbols! So good!
Re: Empirical Bayes for multiple sample sizes
#3Stan is great! Glad to see it on HN. Nice write up too.
Re: Empirical Bayes for multiple sample sizes
#4Although tangentially linked to in the article, David Robinson's Introduction to Empirical Bayes[1] is also an excellent resource. It deals primarily with beta-binomial distributions.
Re: Empirical Bayes for multiple sample sizes
#5That definition of symbols! So good!
I know! I love when math books and papers do that.
Re: Empirical Bayes for multiple sample sizes
#6Although tangentially linked to in the article, David Robinson's Introduction to Empirical Bayes[1] is also an excellent resource. It deals primarily with beta-binomial distributions. [1] http://varianceexplained.org/r/empirical-bayes-book/
It's an excellent blog post, although it's worth emphasizing that it is designed for the binomial case, where you wish to compute the fraction of occurrences within some events, such as batting averages. For continuous variables, however, it makes more sense to use one of the methods described in the original post.
TL;DR: One blog post is for Rotten Tomatoes and the other is for Metacritic.
Re: Empirical Bayes for multiple sample sizes
#7Although tangentially linked to in the article, David Robinson's Introduction to Empirical Bayes[1] is also an excellent resource. It deals primarily with beta-binomial distributions. [1] http://varianceexplained.org/r/empirical-bayes-book/
It's an excellent blog post, although it's worth emphasizing that it is designed for the binomial case, where you wish to compute the fraction of occurrences within some events, such as batting averages. For continuous variables, however, it makes more sense to use one of the methods described in the original post. TL;DR: One blog post is for Rotten Tomatoes and the other is for Metacritic.
Absolutely, and thanks for better defining the distinction.
I really just wanted to point out another solid Empirical Bayes resource, as there's not that many about. Yours and David's make a good combination covering different cases.
Re: Empirical Bayes for multiple sample sizes
#8Technical term is overkill. Just use https://en.wikipedia.org/wiki/Bayesian_average
Re: Empirical Bayes for multiple sample sizes
#9Technical term is overkill. Just use https://en.wikipedia.org/wiki/Bayesian_average
Thank you. As a statistician, the fact that mixed effects models (e.g. does this rater tend to rate high?) are overlooked is, IMHO, a death sentence. Too much nomenclature, too early (link to the table within the text, please, and omit needless words), and with too little attention paid to the value of an external citation.
Also, MCMC for ratings? Surely you jest. If the author had touched on mixed models, then maybe it would make sense. But given the sample sizes involved here, and the noise in the variance estimates, I recommend that the author investigate mixed models tout suite if they do in fact care about the sources of shared and unshared effects on variance. Because that is what mixed models do.
Re: Empirical Bayes for multiple sample sizes
#10Technical term is overkill. Just use https://en.wikipedia.org/wiki/Bayesian_average
Thank you. As a statistician, the fact that mixed effects models (e.g. does this rater tend to rate high?) are overlooked is, IMHO, a death sentence. Too much nomenclature, too early (link to the table within the text, please, and omit needless words), and with too little attention paid to the value of an external citation. Also, MCMC for ratings? Surely you jest. If the author had touched on mixed models, then maybe…
Author here. Please see the section on mixed models in my post. As I mentioned there, I would love if an expert could expand on the relationship between mixed effects and Empirical Bayes.
Regarding MCMC, one of the things I try to emphasize throughout the post is that the best solution depends on your needs (for example if you want a full posterior). In fact, most of the post is devoted to quick and simple methods -- not MCMC -- because they are good enough for most purposes. I welcome your feedback though on how I could make this point clearer.