Astral Codex Ten

Bayes

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Bayes

Article

Bayes is a recurring concept in the Astral Codex Ten archive, appearing 4 times across 4 issues between March 26, 2021 and May 30, 2025. The archive places it in contexts such as “the basal ganglia weighs the evidence using Bayes and flees the predator”; “You can do it using Bayes, but it will be complicated”; “The whole point of all this Bayes stuff is that ‘the middle ground’ is wide”. It most often appears alongside Aella, Scott, 3Blue1Brown.

Metadata

  • Category: Concepts
  • Mention count: 4
  • Issue count: 4
  • First seen: March 26, 2021
  • Last seen: May 30, 2025

Appears In

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Toward A Bayesian Theory Of Willpower
March 26, 2021 · Original source
I've come to disagree with all of these perspectives. I think willpower is best thought of as a Bayesian process, ie an attempt to add up different kinds of evidence.
The economic metaphor here is cute, but the predictive coding community uses a different one: they describe it as representing the "confidence" or "level of evidence" for a specific calculation. So an alternate way to think about lampreys is that the flee-predator region is saying "I have VERY VERY strong evidence that fleeing a predator would be the best thing to do right now." Other regions submit their own evidence for their preferred tasks, and the basal ganglia weighs the evidence using Bayes and flees the predator.
Inline links: predictive coding
Open Thread 167
April 11, 2021 · Original source
1: Thanks to everyone who commented about multiple hypothesis testing. I think the answers to the questions I had were something like 1) the vitamin D study should have used stratified random sampling. 2) NHST is not great at combining experiments, but you could potentially do it through binomial probability or the Westfall-Young method) 3) You can do it using Bayes, but it will be complicated and involve having to calculate the relative probability of the data given various different hypotheses (props to Jacob Falkovich for actually doing the Bayesian calculation; in case you’re wondering, the Bayes factor for my series of ambidexterity experiments is 216). Some good links from the comments: Why Experimenters Might Not Always Want To Randomize, And What They Could Do Instead, What Is Stratified Random Sampling?, Rerandomization: What Is It And When Should You Use It.
Inline links: binomial probability, Westfall-Young, actually doing the Bayesian calculation, Why Experimenters Might Not Always Want To Randomize, And What They Could Do Instead, What Is Stratified Random Sampling?, Rerandomization: What Is It And When Should You Use It
Contra Resident Contrarian On Unfalsifiable Internal States
November 11, 2022 · Original source
I reject this characterization. Everything is a middle ground. The whole point of all this Bayes stuff is that “the middle ground” is wide and worth fighting over. We can have a non-absolute middle ground with 1% probability, a non-absolute middle ground with 99% probability, or anything in between. I’m not doing the morality/etiquette thing of demanding a norm that you believe people, I’m doing an epistemic thing of providing justifications for a prior that you believe people.
Bayes For Everyone
May 30, 2025 · Original source
Eh. You’d think that as someone whose teaching style could be described as “wacky” and “manic” and “unhinged”, I’d be in favor of more silliness in schools. I suppose I am, but it’s important to note that “fun” by itself is unsubstantial. Education is a serious pursuit; souls are on the line. I.I.: Then is it “relevancy” we need? (How could we make Bayesian reasoning relevant to middle schoolers’ everyday lives?) Making what we teach relevant is essential — but be very careful about the assumptions packed into words like “relevant”, “useful”, and “practical”. When most of us hear those, we start thinking about the externals of our students’ lives. (How will this help them get a job? How will this help them become socially savvy?) Pause to consider how some of the most boring topics you learned in school were precisely those that were supposed to be “useful”. Here be dragons!
Then look at who developed Bayesianism further. What community championed it? What sorts of things were driving them? Dear reader, we are that community! And why did we throw ourselves into Bayesian reasoning so fully? Certainly different people can give different answers, but my understanding is that many of us got interested in order to win online arguments against morons. My own start wasn’t particularly “relevant” to anything else I was doing: I got into Bayes to debate the historicity of Jesus. The people I see using it the most these days are mostly partisans (on both sides) of the God wars.
Bayesian reasoning can become confirmation bias on steroids. You have to be humble in your analysis, because there are SO MANY DIFFERENT WAYS IT CAN GO WRONG.