abdullahau

abdullahau's Stars

• py-why/dowhy

  DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

  Language:Python7.4k139489952

• HIPS/autograd

  Efficiently computes derivatives of NumPy code.

  Language:Python7.2k212414912

• mwouts/jupytext

  Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts

  Language:Python6.8k72705401

• FluxML/Zygote.jl

  21st century AD

  Language:Julia1.5k51869212

• StatisticalRethinkingJulia/StatisticalRethinking.jl

  Julia package with selected functions in the R package `rethinking`. Used in the SR2... projects.

  Language:Julia39193933

• baggepinnen/MonteCarloMeasurements.jl

  Propagation of distributions by Monte-Carlo sampling: Real number types with uncertainty represented by samples.

  Language:Julia27457817

• TuringLang/AdvancedHMC.jl

  Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

  Language:Jupyter Notebook2721517344

• stan-dev/stancon_talks

  Materials from Stan conferences

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• betanalpha/knitr_case_studies

  Inference case studies in knitr

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• Shmuma/rethinking-2ed-julia

  Port of Statistical Rethinking (2nd edition) code to Julia

  Language:Jupyter Notebook1317419

• ColtAllen/btyd

  Buy Till You Die and Customer Lifetime Value statistical models in Python.

  Language:Python1169429

• lawmurray/Birch

  A probabilistic programming language that combines automatic differentiation, automatic marginalization, and automatic conditioning within Monte Carlo methods.

  Language:C++11514814

• betanalpha/jupyter_case_studies

  Inference case studies in jupyter

  Language:Jupyter Notebook9310420

• TuringLang/AdvancedVI.jl

  Implementation of variational Bayes inference algorithms

  Language:Julia9053119

• betanalpha/stan_intro

  Draft introduction to probability and inference aimed at the Stan manual.

  Language:TeX8612420

• betanalpha/mcmc_diagnostics

  Markov chain Monte Carlo general, and Hamiltonian Monte Carlo specific, diagnostics for Stan

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• bachmannpatrick/CLVTools

  R-Package for estimating CLV

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• betanalpha/quarto_chapters

  Book Chapter Drafts

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• betanalpha/mcmc_visualization_tools

  Markov Chain Monte Carlo Visualization Tools

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• sidravi1/Blog

  Language:Jupyter Notebook24105

• krassowski/jupyterlab-dagitty

  JupyterLab renderer of dagitty causal diagrams

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• stappit/bayesian-data-analysis

  Berlin Bayesians' solutions to Bayesian Data Analysis, 3rd edition.

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• corriebar/statrethinking_reading_group

  Material for the Berlin Bayesian reading group covering Statistical Rethinking by Richard McElreath

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• arviz-devs/arviz-plots

  ArviZ modular plotting

  Language:Python914758

• zabahana/Customer-LifeTime-Value-Analysis

  Customer lifetime value analysis in python. Using Beta Geometric Negative Binomial Distribution and Gamma-Gamma methods

  Language:Jupyter Notebook9003

• alexpavlakis/clv

  Language:R62114

• jesscyzhao/hb_pareto_nbd

  Implementation of a Hierarchical Bayesian approach to ParetoNBD model using Gibbs sampler and Slice sampling

  Language:Python52

• aaronjg/pnbd_stan

  PNBD/Stan model presented at Stanford Stan User Group March 2018

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• wpbindt/clv-model

  An implementation of Hardie and Fader's (and others') customer lifetime value models using Markov chain Monte Carlo.

  Language:Python2201

• capers/BTYDplus

  Extension to R package BTYD

  Language:R10