Top Tweets for #pyABC
📜 Here is our paper on #pyABC, published in @JOSS_TheOJ.
➡️Paper: https://t.co/XowjY6U0pi
➡️Tool: https://t.co/Ewj2bmWmPg
🧵 We describe various updates to our likelihood-free parameter inference tool.
Just published in JOSS: 'pyABC: Efficient and robust easy-to-use approximate Bayesian computation' https://t.co/fFrwLx1MI0
The latest preprint of our likelihood-free inference tool #pyABC contains very useful features. Lead by the one and only @yannik_schaelte
🧑💻 psst here is our new #pyABC #preprint quickly wrapping up various updates & new features! 🥧🔤
https://t.co/15igJm5iiS
Thanks esp. to Emmanuel Klinger, @EmadAlamoudi , @JanHasenauer, and many users and collab partners!
🧑💻 psst here is our new #pyABC #preprint quickly wrapping up various updates & new features! 🥧🔤
https://t.co/15igJm5iiS
Thanks esp. to Emmanuel Klinger, @EmadAlamoudi , @JanHasenauer, and many users and collab partners!
Especially for high-dim data, unraveling relevant aspects is crucial. #pyABC now provides multiple methods of doing so, via learning inverse ML models. (More details to follow soon!)
https://t.co/XNucyRvOTR
Efficiency+convergence of sequential ABC depend on the acceptance threshold sequence. Besides e.g. quantiles, #pyABC now also implements a scheme analyzing acceptance rate curves, based on an approach by @theosysbio, beneficial to avoid local optima.
https://t.co/GEamdddlf4
Here is our latest preprint on robust adaptive distances for ABC on outlier-corrupted data, based on a method by @dennisprangle. All methods are available in #pyABC. @EmadAlamoudi @JanHasenauer.
➡️ Preprint: https://t.co/fRzwsRa8ew
➡️ Example notebook: https://t.co/rjIqKcy1Bg

Looking forward to talking about likelihood-free inference and #pyABC at the OpenSourceEconomics Meetup at Bonn University, today 18:00 CET. #interdisciplinary https://t.co/6VKeg24n0l
@EmadAlamoudi @FitMultiCell A great opportunity to learn about multi-cellular modeling using @morpheus_lab and #pyABC. Check it out #IbSB2020!
3/4 We @JanHasenauer lab @ICBMunich developed the #HPC ready tool #pyABC (https://t.co/VYXugH6Blb), with e.g. self-tuned distances and proposal kernels. Together with @morpheus_lab we develop the @FitMultiCell platform tailored to multi-cellular systems. #YoungScientistsHMGU

The 2nd #fitmulticell meeting in Heidelberg already showed great progress on the path to data-driven modeling of multi-cellular systems. Check out how we integrate #pyabc and @morpheus_lab at https://t.co/rYOeSjUbrO!

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