1,115 skills found · Page 1 of 38
pymc-devs / PymcBayesian Modeling and Probabilistic Programming in Python
markdregan / Bayesian Modelling In PythonA python tutorial on bayesian modeling techniques (PyMC3)
thu-ml / ZhusuanA probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
uber / OrbitA Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.
xinychen / Awesome Latex DrawingDrawing Bayesian networks, graphical models, tensors, technical frameworks, and illustrations in LaTeX.
arviz-devs / ArvizExploratory analysis of Bayesian models with Python
dotnet / InferInfer.NET is a framework for running Bayesian inference in graphical models
paul-buerkner / Brmsbrms R package for Bayesian generalized multivariate non-linear multilevel models using Stan
TheEconomist / Us Potus ModelCode for a dynamic multilevel Bayesian model to predict US presidential elections. Written in R and Stan.
bambinos / BambiBAyesian Model-Building Interface (Bambi) in Python.
pymc-labs / Pymc MarketingBayesian marketing toolbox in PyMC. Media Mix (MMM), customer lifetime value (CLV), buy-till-you-die (BTYD) models and more.
google / Lightweight MmmLightweightMMM 🦇 is a lightweight Bayesian Marketing Mix Modeling (MMM) library that allows users to easily train MMMs and obtain channel attribution information.
brendenlake / BPLBayesian Program Learning model for one-shot learning
neka-nat / ProbregPython package for point cloud registration using probabilistic model (Coherent Point Drift, GMMReg, SVR, GMMTree, FilterReg, Bayesian CPD)
jluttine / Tikz BayesnetTikZ library for drawing Bayesian networks, graphical models and (directed) factor graphs in LaTeX.
lindermanlab / SsmBayesian learning and inference for state space models
ericmjl / Bayesian Stats Modelling TutorialHow to do Bayesian statistical modelling using numpy and PyMC3
bayesflow-org / BayesflowA Python library for efficient Bayesian modeling with deep learning
easystats / BayestestR:ghost: Utilities for analyzing Bayesian models and posterior distributions
yaringal / DropoutUncertaintyExpsExperiments used in "Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning"
