probability
pyro
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probability | pyro | |
---|---|---|
10 | 9 | |
4,057 | 8,315 | |
0.6% | 0.7% | |
9.3 | 8.4 | |
about 15 hours ago | 5 days ago | |
Jupyter Notebook | Python | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
probability
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How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have?
TensorFlow-Probability
- [P] Any good resources which can help me with Multivariate Time Series Forecasting using Probabilistic Machine Learning?
- Is anyone here working in uncertainty estimation in neural networks?
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[Q] Sociology PhD Student with Interest in Statistical Programming/Data Science
As others have said, R for academia, Python for industry. However, i'd also throw Stan into the mix, along with other PPL frameworks like Tensorflow Probability and Pyro. The latter two will require you to learn Python first, though.
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What is Probabilistic Programming?
This tutorial explains what is probabilistic programming & provides a review of 5 frameworks (PPLs) using an example taken from Chapter 4 of Statistical Rethinking by Dr. Richard McElreath. Frameworks (PPLs) reviewed are - Stan (https://mc-stan.org/) PyMC3 (https://docs.pymc.io/) Tensorflow Probability (https://www.tensorflow.org/probability) Pyro/NumPyro (https://pyro.ai/) Turing.jl (https://turing.ml/stable/) I also provide the basic review of a great library called arviz (https://arviz-devs.github.io/arviz/), which can be used for all the above-mentioned PPLs to do Exploratory Data Analysis of Bayesian Models. Here is the link to the notebook in which I have implemented the example model using the above Frameworks/PPLs https://colab.research.google.com/drive/1zgR2b0j2waGi1ppnIe1rw7emkbBXtMqF?usp=sharing
pyro
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Show HN: Designing Bridges with PyTorch
Mostly I use pytorch for statistical modeling https://pyro.ai . Under the hood that package uses a lot of Monte Carlo integration and variational methods (i.e. integration by optimization). It does support neural nets, but probably >80% of pyro users stick to simpler hierarchical Bayesian models.
- The Jupyter+Git problem is now solved
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[P] torchegranate: a PyTorch rewrite of the pomegranate library for probabilistic modeling
Can you compare this to Pyro, which is also built on top of PyTorch?
- [Q] Updated book or review paper on MCMC methods
- Is anyone here working in uncertainty estimation in neural networks?
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[D] Do you train and deploy models using just one framework or multiple frameworks at work?
Using pyod, statmodels, scikit-learn, Tensorflow and pyro.ai (that is using PyTorch as backend). I always use the same framework for training and for production.
What are some alternatives?
PyMC - Bayesian Modeling and Probabilistic Programming in Python
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
scikit-learn - scikit-learn: machine learning in Python
trueskill - An implementation of the TrueSkill rating system for Python
tensorflow - An Open Source Machine Learning Framework for Everyone
Keras - Deep Learning for humans
MLflow - Open source platform for the machine learning lifecycle
Sacred - Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.
PaddlePaddle - PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
LightFM - A Python implementation of LightFM, a hybrid recommendation algorithm.