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In my field the embrace of Bayesian methods seems to have come because of increases in computing power, rather than a decrease in "opposition."

• rspeer 10y ago
  I'm guessing what you're talking about is using Bayesian techniques to compute something useful, which nobody will stop you from doing. The part that has opposition is using a Bayesian justification for the experimental results in a paper -- presenting a conclusion about how we should update our beliefs because of the experiment, instead of a p-value. Right now it seems pretty common to translate Bayesian experimental results sloppily into frequentist ones to get them past reviewers.
  • jtraffic 10y ago
    > Right now it seems pretty common to translate Bayesian experimental results sloppily into frequentist ones to get them past reviewers. This is interesting. I'm glad for a different perspective. I chalk this up to differences across fields, because my experience is so different. My advisor learned Bayesian statistics from Arnold Zellner a few decades ago, and his synopsis of the class was: "It was great in theory, but we could't implement it." Once computers made if more possible, it seems to have spread rather quickly, possibly because computing power was the last piece of a mostly complete puzzle. It's almost to the point that reviewers prefer the Bayesian approach, in my neck of the academic woods.
    • Fomite 10y ago
      This is pretty much my experience as well - accessible Bayesian methods (and now accessible causal inference models) were a major portion in those methods being adopted.