Data science analyses delving into National Hockey League (NHL) ice hockey statistics
🌌 Measure nuclear symmetry energy from pulsar glitches using vortex oscillation spectroscopy for insights into the nuclear equation of state.
Astronomical time series analysis with JAX
A Bayesian hierarchical model for source-nu associations
Colibri is an adaptable, open-source platform for flexible Parton Distribution Function (PDF) fitting. It is a reportengine app that performs PDF fits with any given parametrisation.
Single Point Object Tracking Algorithms
Gaussian processes in JAX and Flax.
Kullback-Leibler projections for Bayesian model selection in Python
PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
Bayesian Conjugate Models in Python
Analyze and model weekly calendar distributions using latent components
Bayesian Modeling and Probabilistic Programming in Python
A fully probabilistic framework for Tensor Network Kernel machines that applies sparsity-inducing hierarchical priors to Tensor Network factors, allowing automatic inference of tensor rank and feature dimensions while identifying the most predictive features to enhance interpretability.
BOBE (Bayesian Optimation for Bayesian Evidence) is a package for Bayesian model selection with expensive likelihood functions, developed for applications to cosmology.
A unified framework for stochastic sampling packages and gravitational-wave inference in Python.
Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.
Python-first access to R’s brms with proper parameter names, ArviZ support, and cmdstanr performance. The easiest way to run brms models from Python.
An AI-powered Bayesian generative modeling approach for causal inference in observational studies
An open-source JAX-based gravitational-wave population inference toolkit for parametric models
PyRate is a program to estimate speciation, extinction, and preservation rates from fossil occurrence data using a Bayesian framework.
Add a description, image, and links to the bayesian-inference topic page so that developers can more easily learn about it.
To associate your repository with the bayesian-inference topic, visit your repo's landing page and select "manage topics."