Qiita - 人気の記事Aug 1, 2025

本番環境にベイズモデルをデプロイする方法、全部話します
https://qiita.com/Gotoubun_taiwan/items/d543deb6f2b7a2763797?utm_campaign=popular_items&utm_medium=feed&utm_source=popular_items

#qiita #Python #R #Stan #PyMC #Numpyro

本番環境にベイズモデルをデプロイする方法、全部話します - Qiita

こんにちは、事業会社で働いているデータサイエンティストです。 皆さん、せっかく良いベイズ統計・ベイズ機械学習モデルを作ったのに、「うまくデプロイできない…」という状況に直面したことはありませんか？ 本記事では、ベイズモデルの本質を押さえたシンプルなトイモデルを用いて、...

Qiita
Show threadAdam FleischhackerAug 11, 2023

Simple #rstats install - works on ALL platforms (see readme for more). Just four steps. Please test this #numpyro version before I release to CRAN.🙏

install.packages("remotes")
remotes::install_github("flyaflya/causact")
library(causact)
install_causact_deps()

Adam FleischhackerAug 11, 2023

The {causact}📦 is bringing #numpyro speed to #rstats. Learning computational #Bayesian inference has never been easier.

🐣Install with ease
📊Build visual models with DAGs
🚅Automate Inference at #numpyro speed

#numpyro now available via github:
https://github.com/flyaflya/causact

GitHub - flyaflya/causact: causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.

causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output. - GitHub - flyaflya/causact: causact: R package to a...

GitHub
GripNewsJul 28, 2023
🌘 Pyro
➤ Pyro的特點和安裝指南
✤ https://pyro.ai/
Pyro是一種通用的概率編程語言，使用Python編寫，並在後端由PyTorch支持。它具有可擴展性、最小化和靈活性的特點。最新版本的NumPyro使用JAX進行自動微分和JIT編譯，速度提升了100倍以上。該文還提供了安裝和使用Pyro的指南。
+ 我對Pyro的速度提升感到興奮！
+ 這篇文章提供了很好的安裝指南，讓我能夠輕鬆使用Pyro。
#Pyro #NumPyro #概率編程
Pyro

Deep Universal Probabilistic Programming

Adam FleischhackerApr 19, 2023

I've been making progress on my #Bayesian inference in #python book draft. Latest addition is the multi-level modelling chapter. Check it out and then post your feedback here. Thanks! #numpyro

BOOK Link: https://www.persuasivepython.com/13-multilevel

Persuasive Python - 14  Multi-Level Modelling

PyMC developersMar 21, 2023

🎉 Exciting news for PyMC users! 🎉

🔥 The latest PyMC update now allows you to choose from different NUTS samplers provided by external packages like #nutpie, #blackjax, and #numpyro using the new nuts_sampler kwarg in the sample() method. This means you can now take advantage of different samplers and their unique features to achieve the best performance for your model. 🚀

⚡️ And let me tell you, nutpie is wicked fast! 😱

👉 Give it a try and see how it improves your Bayesian analysis workflow! 💻