pure-Python HistFactory implementation with tensors and autodiff
-
Updated
Jul 3, 2024 - Python
pure-Python HistFactory implementation with tensors and autodiff
Estimagic is a Python package for nonlinear optimization with or without constraints. It is particularly suited to solve difficult nonlinear estimation problems. On top, it provides functionality to perform statistical inference on estimated parameters.
Ambrosia is a Python library for A/B tests design, split and result measurement
A package for statistically rigorous scientific discovery using machine learning. Implements prediction-powered inference.
MCMC sample analysis, kernel densities, plotting, and GUI
Statistics tools and utilities.
Hypothesis and statistical testing in Python
Statistical inference on machine learning or general non-parametric models
Code and data for the KDD2020 paper "Learning Opinion Dynamics From Social Traces"
Perform inference on algorithm-agnostic variable importance in Python
[TNNLS 2022] Significance tests of feature relevance for a black-box learner
Generalized Additive Models in Python.
A Bayesian model of series convergence using Gaussian sums
Cegpy (/segpaɪ/) is a Python package for working with Chain Event Graphs. It supports learning the graphical structure of a Chain Event Graph from data, encoding of parametric and structural priors, estimating its parameters, and performing inference.
Code and data for the CIKM2021 paper "Learning Ideological Embeddings From Information Cascades"
Off-Policy Interval Estimation withConfounded Markov Decision Process
Reproduce analyses from "Efficient nonparametric statistical inference on population feature importance using Shapley values"
My Personal Machine Learning and Data Science Training Repository
Code implementing Integrator Snippets, joint work with Christophe Andrieu and Chang Zhang
A little exploration of R's power for statistical inference
Add a description, image, and links to the statistical-inference topic page so that developers can more easily learn about it.
To associate your repository with the statistical-inference topic, visit your repo's landing page and select "manage topics."