Interview on ”Bayesian Statistics the Fun Way”
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Interview on ”Bayesian Statistics the Fun Way”
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Re: Interview on ”Bayesian Statistics the Fun Way”
#2I love the idea of making an approachable version of Ed Jaynes’s classic.
Re: Interview on ”Bayesian Statistics the Fun Way”
#3I got in an argument with a friend (a mechanical/electrical engineer) who knew about bayesian statistics. My other friend, a PhD in statistics, whom I had many discussions about because both personal interest and work interests, had supplied me with my modicum of statics knowledge.
My engineer friend called my PhD friend a "frequentist", like it was a dirty word, despite only having one, maybe two, classes in college about bayesian math/statistics/whatever (my ignorance).
This quote jumped out at me in the article:
"I wanted to write a book on Bayesian statistics that really anyone could pick up and use to gain real intuitions for how to think statistically and solve real problems using statistics."
In the context of the statement, it sounds like he is claimin any non-bayesian statistics is useless (or less valuable/reliable at best) than other forms of statistical analysis?
Re: Interview on ”Bayesian Statistics the Fun Way”
#4I got in an argument with a friend (a mechanical/electrical engineer) who knew about bayesian statistics. My other friend, a PhD in statistics, whom I had many discussions about because both personal interest and work interests, had supplied me with my modicum of statics knowledge. My engineer friend called my PhD friend a "frequentist", like it was a dirty word, despite only having one, maybe two, classes in college…
Having known Will when I lived in Reno I'm certain your focus should be on "anyone could pick up and use" and not any statement about the usefulness of other approaches. The Will I know is fundamentally about teaching things in very easy to understand ways, and curious about all approaches to solving a problem.
Re: Interview on ”Bayesian Statistics the Fun Way”
#5I got in an argument with a friend (a mechanical/electrical engineer) who knew about bayesian statistics. My other friend, a PhD in statistics, whom I had many discussions about because both personal interest and work interests, had supplied me with my modicum of statics knowledge. My engineer friend called my PhD friend a "frequentist", like it was a dirty word, despite only having one, maybe two, classes in college…
That's not how I'm reading that quote at all. Saying Bayesian stats can solve real problems doesn't imply frequentist stats can't.
Re: Interview on ”Bayesian Statistics the Fun Way”
#6I got in an argument with a friend (a mechanical/electrical engineer) who knew about bayesian statistics. My other friend, a PhD in statistics, whom I had many discussions about because both personal interest and work interests, had supplied me with my modicum of statics knowledge. My engineer friend called my PhD friend a "frequentist", like it was a dirty word, despite only having one, maybe two, classes in college…
It just reads to me like he wants to make statistics accessible to a wide audience.
Re: Interview on ”Bayesian Statistics the Fun Way”
#7As someone who uses statistics all the time at work, I sympathize so much with this article and greatly enjoyed it. Every time I try to introduce a Bayesian prior, coworkers either look at me like I'm crazy (because they've never heard of or used Bayesian stats) or like I've suddenly gone soft and introduced a bunch of nebulous, touchy-feely context into the objective truth (if they're dedicated frequentists).
Then we promptly switch back to p-values of .05, a lot of the time not even bothering with a statistical power calculation. I've had better success with introducing power, though. I suspect that's because we can fit it into the existing frequentist framework.
Re: Interview on ”Bayesian Statistics the Fun Way”
#8Can anyone recommend a 'Bayesian statistics the hard way' book?
Re: Interview on ”Bayesian Statistics the Fun Way”
#9As someone who uses statistics all the time at work, I sympathize so much with this article and greatly enjoyed it. Every time I try to introduce a Bayesian prior, coworkers either look at me like I'm crazy (because they've never heard of or used Bayesian stats) or like I've suddenly gone soft and introduced a bunch of nebulous, touchy-feely context into the objective truth (if they're dedicated frequentists). Then w…
> like I've suddenly gone soft and introduced a bunch of nebulous, touchy-feely context into the objective truth
This drives me nuts. If you haven't, check out the paper "Beyond subjective and objective in statistics" by Gelman and Hennig (2017).
Right at the beginning they make the point that any analysis includes external information in many ways, such as adjusting variables for imbalance, how we deal with outliers, regularization, etc.
Especially if you're doing any sort of causal inference, you're usually making strong assumptions before estimating your model, even just in terms of which variables are included and how they're connected. The idea that priors are somehow ruining an "objective" model is just absurd to me. You're already making so many other decisions about your model that will affect estimates and your interpretation of them. Priors seem like another perfectly reasonable decision to have to make as well, with the benefit of getting results that I think in general are must more easily understood by a lay audience. (E.g., I don't think I've ever encountered someone not on my data science team that actually understands what a p-value is. But people are much better at understanding when I say, there's an X percent chance that there is a positive effect here.)
Re: Interview on ”Bayesian Statistics the Fun Way”
#10As someone who has a master's degree in statistics and often uses Bayesian statistics, I think we should not focus on whether one is a Bayesian or a frequentist, but rather be pragmatic and take the most practical approach to solving a statistical problem. Moreover, I think statistical education should start with frequentist concepts and then extend them to the Bayesian framework since the likelihood plays also a major role in obtaining the posterior distribution. In my opinion, this progression is much more natural than starting fully Bayesian.