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bambi reviews and mentions
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Bayesian Structural Equation Modeling using blavaan
It is much less challenging with Bambi[1] and brms[2].
[1] https://bambinos.github.io/bambi/
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Ask HN: What Are You Learning?
I’m trying to learn statistics. I’m up to implementing regressions in python using sci-kit learn.
I was playing around with Bayesian modelling last night with https://bambinos.github.io/bambi/ But I’m not really sure how to interpret the outputs.
Always open to reading about learning resources/books/videos/courses from others.
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how can I build a regression model which is penalised for moving away from an assumed set of coefficients?
I would suggest using Python's bambi; it is based on PyMC and it is very straightforward to use. We simply define our priors argument as a dictionary (quite literally: my_priors = {"feature_1": bmb.Prior("Normal", mu=4, sigma=4), "feature_n": bmb.Prior("Normal", mu=0.4, sigma=0.4)}) when creating our Bambi Model object and we are ready to go. They have a lot of worked exampling in their website.
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Which not so well known Python packages do you like to use on a regular basis and why?
For those interested in Bayesian modeling in Python we also have Bambi https://github.com/bambinos/bambi
- Release Bambi 0.6.0 · bambinos/bambi
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A note from our sponsor - WorkOS
workos.com | 19 Apr 2024
Stats
bambinos/bambi is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of bambi is Python.