Alternatives and similar repositories for PyData-talk---Intro-to-PyMC3

Users that are interested in PyData-talk---Intro-to-PyMC3 are comparing it to the libraries listed below

• AlexIoannides / lime-interpretable-ml
  An example of how the LIME algorithm can be used to provide real-world insight into the decision processes of a 'black-box' machine learn…
  ☆15Updated 6 years ago
• CamDavidsonPilon / PyDataNY_2019_tutorial
  Repo for PyData 2019 Tutorial - New Trends in Estimation and Inference
  ☆25Updated 5 years ago
• stefan-jansen / topic-modeling
  ☆14Updated 2 years ago
• fastforwardlabs / structural-time-series
  Structural Time Series on US electricity demand data
  ☆22Updated 4 years ago
• amit-sharma / splitdoor-causal-criterion
  A method for estimating causal effects in time-series data. Uses available data to automatically find natural experiments for identifying…
  ☆15Updated 5 years ago
• pbstark / MX14
  Short course on nonparametric inference in auditing and litigation, XXIX Foro Internacional de Estadistica, Puebla, MX
  ☆15Updated 8 years ago
• firmai / python-for-finance
  ☆14Updated 6 years ago
• eigenfoo / random
  This is all my random garbage.
  ☆26Updated 2 years ago
• khakieconomics / stanecon_short_course
  Notes for short course on econometrics in Stan
  ☆10Updated 8 years ago
• fonnesbeck / PyMC3_EUSS
  Course in Probabilistic Programming in Python for the 2018 EU Summer School
  ☆23Updated 7 years ago
• AlxndrMlk / PyDataGlobal2021
  ☆12Updated 3 years ago
• FlorianWilhelm / bhm-at-scale
  🪜 Bayesian Hierarchical Models at Scale
  ☆51Updated 3 years ago
• parsing-science / pydata_nyc_nov_2017
  ☆30Updated 7 years ago
• vcerqueira / vest
  Automatic Feature Engineering for Time Series
  ☆17Updated 2 years ago
• oegedijk / rule_estimator
  A scikit-learn compatible estimator based on business-rules with interactive dashboard included
  ☆28Updated 3 years ago
• vcerqueira / MLforForecasting
  Machine Learning vs Statistical Methods for Time Series Forecasting: Size Matters
  ☆41Updated 5 years ago
• SimonOuellette35 / Introduction_to_PyMC3
  ☆39Updated 6 years ago
• PhilChodrow / cos_2017
  Course materials for the ORC's 2017 IAP course, "Computing in Optimization and Statistics"
  ☆19Updated 7 years ago
• pymc-learn / pymc-learn-book
  Book: Practical Probabilistic Machine Learning in Python
  ☆10Updated 4 years ago
• gumption / pydata-simpsons
  Content associated with a PyData Seattle 2017 tutorial on Unevenly spaced time series analysis of The Simpsons using pandas
  ☆15Updated 8 years ago
• KevinFasusi / supplychainpy_notebooks
  Jupyter Notebooks for supplychainpy
  ☆22Updated 8 years ago
• aschleg / hypothetical
  Hypothesis and statistical testing in Python
  ☆64Updated 4 years ago
• FMZennaro / CausalInference
  Illustration of counterfactual inference following Ferenc Huszar example
  ☆13Updated 10 months ago
• yrbszhsh / Principles-of-Artificial-Intelligence-Learning-Algorithms
  My work on UCSD CSE 250B Principles of Artificial Intelligence: Learning Algorithms
  ☆13Updated 5 years ago
• MSRDL / TSA
  Time Series Anomaly Detection Toolkit
  ☆22Updated 6 years ago
• firmai / ml-fairness-framework
  FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)
  ☆71Updated 3 years ago
• LeihuaYe / Causal-Inference-Using-Quasi-Experimental-Methods
  Causal Inference Using Quasi-Experimental Methods
  ☆20Updated 4 years ago
• jameslamb / lightgbm-dask-testing
  Test LightGBM's Dask integration on different cluster types
  ☆12Updated last week
• chaaland / bloomberg-ml-course
  Solutions to machine learning HW from bloomberg ml course
  ☆11Updated 6 years ago
• microsoft / AdversarialGMM
  Minimax Estimation of Conditional Moment Models
  ☆30Updated 2 years ago