That's not a Bayesian approach.

There are degrees of belief attached to each aspect and the proposal works exactly like existing acknowledged-as-bayesian approaches eg spamassassin does. So how isn't it?

A Bayesian approach would learn the degrees of belief empirically from examples of spam and "ham" messages.

There are other machine learning approaches that would also learn from data - specifically, a Bayesian approach would use Bayes theorem.

Well yes, you'd base those weights on actual, human confirmed account bans.

Nothing about my post ever suggested the value was arbitrary. You've just assumed that.

The inclusion of arbitrary values suggested you would actually use those values.

The inclusion of example values was to act as an example. Nothing was stated about where they were derived from. It would be logical to derive them from actual frequency in real life moderation scenarios. Please don't make bad faith assumptions.

Nothing in your post suggested that they were merely examples, nor that they would be derived empirically.

And in any case, a Bayesian approach would require more than a handful of criteria.

> Sorry, I didn't mean to presume they weren't based on values extracted from real data. I'm worried about people misusing CS terms, but I can see I kind of jumped the gun there.

No problem, happens to the best of us.

Now you should apologize for putting words in my mouth, as well as for wording your post poorly.

I'm not going to apologize for reading the words you wrote, nor for not being able to read your mind.

I didn't expect you to read my mind, just the actual post ;)