bvdmitri

[Lazy Dynamics @LazyDynamics]

🚀 What do Bayesian inference and skydiving have in common? Both demand trust under uncertainty. Our CTO @bvdmitri used RxInfer to clean up noisy pose estimates from his 500th skydive — showing how probabilistic inference fills the gaps where standard ML fails #Bayes #Skydiving

[ReactiveBayes @ReactiveBayes]

🎉 Big news: RxInfer.jl is now a @NumFOCUS Affiliated Project! Born from PhD research on reactive message passing, it’s now an open-source tool for fast, scalable Bayesian inference — and part of a world-class ecosystem #JuliaLang #BayesianInference #NumFOCUS #OpenSource

[Lazy Dynamics @LazyDynamics]

Bayesian Inference in the browser? Yup. With new RxInfer TypeScript SDK, enabling real-time, client-side probabilistic reasoning. Think: adaptive UIs, privacy-first personalization and more. Interested in bringing Bayesian intelligence to the frontend? Let's talk.
#RxInfer #WebAI

[ReactiveBayes @ReactiveBayes]

@LazyDynamics just dropped a Python SDK for RxInferServer — call RxInfer models from Python, fast.
Modeling in Python? Not yet. But this is a big first step.

Check the example https://t.co/sfwZMO50Uy

Based on RxInferServer https://t.co/zZ4Hqa8bhT

[ReactiveBayes @ReactiveBayes]

🚀 RxInfer v4.0.0 is here! New features include performance tracking, session monitoring & sharing, and improved HMM/POMDP examples. Breaking changes:
Transition → DiscreteTransition, MatrixDirichlet → DirichletCollection. Try it now! #MachineLearning #BayesianInference

[ReactiveBayes @ReactiveBayes]

We've just launched a new version of RxInfer.jl! 🎉 Check out our more intuitive DSL, thanks to @bvdmitri & @wouter_nuijten. Excited for you to try the simplified model specs and nested models. Kudos to the team! Next up: expanding inference by Q2 end.

#JuliaLang #RxInfer

[Bert de Vries @bertdv0]

We are proud to release RxInfer, https://t.co/NlKv8hxf4h, a Julia package for fast and scalable Bayesian inference in probabilistic models. This toolbox is very suited for fast online inference in freely definable non-linear state-space models. #ActiveInference #FEP #

[Bert de Vries @bertdv0]

We're developing a very efficient variational Bayesian inference toolbox, based on a reactive programming framework, see github https://t.co/LhppM0qAtq and paper https://t.co/zRyBsKr6WF #FEP #bayes #inference

[Bert de Vries @bertdv0]

Great tutorial by @bvdmitri on scalable Bayesian inference by reactive message passing in factor graphs
https://t.co/qxTLkebLYc #messagepassing #factorgraph

[Mikhail Shugay @antigenomics]

Afaik, [https://t.co/3mQrP3XfQc] is the first large study to this date reporting both T-cell receptor (TCR) sequences and their cognate #SARS-CoV-2 epitopes. These #COVID-19 #SARS-CoV-2 specific TCR sequences are now available in our latest VDJdb release https://t.co/vJfoWtkAof

[Bert de Vries @bertdv0]

We just released our own implementation of reactive programming extensions for Julia. It is almost an exact port of RxJS from ReactiveX (not all features has been implemented yet). Gladly invite you to check it out: https://t.co/nt2vxlwaDv #julialang #rxjs #reactive

[Mikhail Shugay @antigenomics]

By the way we now have over 20,000 specific T-cell receptor sequences in VDJdb (https://t.co/C4O8wEBXNi).