\mathfrak{Michael "Shapes Dude" Betancourt}
@betanalpha
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Once and future physicist masquerading as a statistician. Reluctant geometer. @mcmc_stan developer. Support my writing at https://t.co/Ut25MyjIAy. He/him.
New York, NY
Joined September 2009
There are many ways to follow along my work, all of which are kept up to date at https://t.co/42LIy5NltR.
betanalpha.github.io
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Posting all of my silly figures over on the cielo azul under the same username.
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This chapter was made possible by my supporters on patreon dot com, https://t.co/qLBdEwM1UB. If my freely-available writing makes your work easier and you’d like to help sustain more writing then consider supporting today.
patreon.com
writing about modeling building and statistical inference.
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In this chapter I cover the foundations of probabilistic mixture modeling, from the marginalization of categorical variables to numerically stable implementations and inferential degeneracies. Along the way I also discuss the discrete and continuous inflation models.
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<Trying to decide between two data generating processes> ¿Porqué no los dos? <celebration> It’s time for mixture models. HTML: https://t.co/UnRCdC2LHR PDF:
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If you need a distraction tomorrow then note that you can still register for my four hour hierarchal modeling course, https://t.co/0gL69z9oRv. All you need to do is make a donation to one of two charities doing important work in Gaza right now.
betanalpha.github.io
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Last chance to join us on Wednesday to learn about the intimate details of hierarchical modeling while also contributing to two important causes, https://t.co/0gL69z9oRv.
betanalpha.github.io
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<looks around apprehensively> So everyone is going to take all of this polling skepticism and critique and apply it to analyses in their own field, perhaps even their own analyses, right? RIGHT?
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Last chance to join us on Wednesday to learn about the intimate details of hierarchical modeling while also contributing to two important causes, https://t.co/0gL69z9oRv.
betanalpha.github.io
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A great reminder not to take arrows in diagrams for granted: what do they mean? what assumptions do we make about them? how reasonable are these assumptions? what happens if they fail?
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I'm still trying to find a good term for studying the range of behaviors in data simulated from a model. I'd appreciate feedback on a few candidates, https://t.co/cPKHdoAYrj.
patreon.com
Get more from Michael Betancourt on Patreon
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Damn, my upcoming hierarchical modeling short course next week, https://t.co/0gL69z9oRv, has raised over 2000 USD for World Central Kitchen and Doctors Without Borders. There's still time to join and spread the word.
betanalpha.github.io
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If you’re interested in more work like this then consider dropping by https://t.co/qLBdEwLu53 to see some of the opportunities to support my work.
patreon.com
writing about modeling building and statistical inference.
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