We're especially interested in experts with deep, hands-on experience in the following area: Computational Bayesian Statistics and Applied Mathematics Working with libraries across Bayesian statistics, including PyMC, PyStan, PyJAGS, and CmdStanPy; applied mathematics and

Stan、名前は聞くけど触ったことないなと思っていたところ、Pystanなるものがあると知りアラッッッとなるなど

ベイズ統計の勉強を続けてますが、またすぐ自分が何を学んでいるのか見失ってしまいます🫠 教材に沿ってPystanでモデルを書いていて、ふと、尤度関数と事前分布はどれ…？と迷子状態😅 でも今は、こんなときGeminiが助けてくれるので本当に助かります！ ＃データサイエンス

ベイズ統計の勉強をUdemyでしていて、コードを書くとき使っていたgoogle ColabではPystanをうまく使えない😅 結果、Geminiに教えてもらいながら、cmdstanpy を使うことにしました。 しかし使い方はGeminiがよく間違えるので、ソースコードを確認してひとまずはクリア😁 #データサイエンス

Developing #BayesianInference methods for complex scientific problems? #EuroSciPy2025 is seeking original work on Hamiltonian Monte Carlo, variational inference, and statistical modeling in #Python. #CfP: pretalx.com/euroscipy-2025/c… #ScientificPython #PyMC #PyStan #EuroSciPy

pystan単体だとRhatが出せんからarvizで出すんだけどベクトルになってると外す必要ありと

pystanいい感じ

Essential Python libraries everyone should know: Data Manipulation Polars: A blazingly fast DataFrames library for Python. Modin: A distributed DataFrame library for Python. Vaex: Handles large datasets efficiently by using memory-mapped files. NumPy: Fundamental package for scientific computing in Python. Pandas: Data manipulation and analysis tool. CuPy: NumPy-like API accelerated with CUDA. Datatable: A Python library for manipulating tabular data. Data Visualization Plotly: Interactive plotting library for Python. Geoplotlib: A Python library for creating geographical plots. Pygal: A dynamic SVG charting library. Altair: Declarative statistical visualization library for Python. Matplotlib: Comprehensive 2D plotting library for Python. Seaborn: Statistical data visualization based on matplotlib. Folium: Makes beautiful, interactive maps with Python and Leaflet.js. Bokeh: Interactive visualization library for large datasets. Statistical Analysis SciPy: Library for scientific computing and technical computing. PyMC3: Probabilistic programming in Python. PyStan: Bayesian inference using the No-U-Turn sampler (NUTS). Statsmodels: Statistical modeling and econometrics in Python. Lifelines: Survival analysis in Python. Pingouin: Statistical analysis in Python based on pandas and SciPy. Machine Learning Jax: Composable transformations of Python NumPy programs. Keras: High-level neural networks API, running on top of TensorFlow, Theano, or CNTK. Theano: Numerical computation library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. TensorFlow: Open-source machine learning framework for everyone. Pytorch: Open source machine learning library used for applications such as computer vision and natural language processing. XGBoost: Gradient boosting library used for classification, regression, and ranking problems. Scikit-learn: Machine learning in Python. Natural Language Processing (NLP) NLTK: A platform for building Python programs to work with human language data. Textblob: Simplified text processing for Python. Bert: Bidirectional Encoder Representations from Transformers. Genism: Topic modeling and document similarity analysis. spaCy: Industrial-strength NLP in Python. Polyglot: Multilingual text processing. Database Operation Dask: Parallel computing in Python. Koalas: A pandas API on top of Apache Spark. PySpark: The Python API for Spark. Ray: A fast and simple framework for building and running distributed applications. Kafka: A distributed streaming platform. Hadoop: Distributed storage and processing of big data using the MapReduce programming model. Time Series Analysis sktime: A unified framework for machine learning with time series. Prophet: Forecasting at scale. Darts: A Python library for time series analysis. Kats: A time series forecasting library developed by Kaggle. AutoTS: Automatic time series models builder and related utilities. tsfresh: Automatic extraction of relevant features from time series. Web Scraping Beautiful Soup: A library for parsing HTML and XML documents. Scrapy: An open-source and collaborative web crawling framework for Python. Octoparse: A free client-side web scraping tool. Selenium: Automates actions in web browsers.

pymcにこだわらずに数式そのまま記述できるpystanとかを使えハゲと言われそう

This is nice. Benchmarks makes sense to me with some reservations. Unfortunately I find posteriordb's current state too tied to Stan and (best of my knowl.) lacking vectorization support at least in PyStan. Also find PyStan or others not useful/a pain for ppl developing methods

pystanでもいいですね！！ ただ、Pystanは割とライブラリ間の競合が激しいのです……()

does anybody have a good recommendation for a versatile and transparent python package for MCMC simulations? pymc, pystan and pyro are complex languages by themselves, the former with a lot of limitations. tfprob suffers from being built upon tensorflow... so, what else is there?

Um livro que gostei bastante e pode ser utilizado como apoio ao livro do Andrew é este, fala tanto do rStan como do pystan.

For Pythonistas: the Stan code in "Bayesian Sports Models in R" will work exactly the same using pystan as it will in R with rstan. Just adapt the data wrangling and simulation functions and you'll be good to go 🫡

It would be highly effective. Stan code is fully compatible with Python (using pystan), so all you need to do is approximate the data wrangling and simulation functions and you should have a very close translation of what we've done in R. pystan.readthedocs.io/en/lat…

書籍「Python時系列分析クックブック」実践中！ prophet環境を作ったけどエラー解消できず、残念ながら断念… 副産物✨ ①pystanとcmdstanpyの環境が出来た ⇒stanを頑張ろうかな… ②matplotlib日本語化 ⇒python 3.12ではmatplotlib_fontjaを使う github.com/ciffelia/matplotl… #のんびり統計 #Python

ゼミにはこれくらいの本がいいのかも / いまさら聞けないPythonでデータ分析 多変量解析,ベイズ統計分析(PyStan,PyMC) amzn.asia/d/ixmduWJ

Rで始めるデータサイエンスなりPystanで〜とかを10年弱前に学び始め、統計検定とかも受かったり、因果推論やら自然言語領域も興味をもってbertとか本買って読んでたらchat gptで衝撃を受け〜 時が経ち今は何故かどう見ても有意じゃない2つの差から考察提言してる 金の為に魂売りすぎた感

ベイズモデリングPyStanがいまいちだったのでRStanでやっていましたが、PyMCを見るとかなりこなれた印象がありますね。 これならPythonでベイズモデリングやっても良いかも。

While reading the story about Klári von Neumann writing the first code for Monte Carlo simulations I was helping a co-worker in his struggles to get PyStan (Monte Carlo Bayesian stuff) installed on Windows. 77 years later and we haven't nailed the first use case