canyon289 / ssm_book_clubLinks
☆17Updated 2 years ago
Alternatives and similar repositories for ssm_book_club
Users that are interested in ssm_book_club are comparing it to the libraries listed below
Sorting:
- Tutorials and sampling algorithm comparisons☆75Updated this week
- Bayesian Bandits☆68Updated last year
- Recursive Bayesian Estimation (Sequential / Online Inference)☆59Updated last year
- Structural Time Series in JAX☆199Updated last year
- Neat Bayesian machine learning examples☆58Updated 2 months ago
- Jax SSM Library☆49Updated 2 years ago
- Course material for the PhD course in Advanced Bayesian Learning☆59Updated 5 months ago
- Lectures on Quantitative Economics Using JAX☆41Updated last week
- Gaussian Process Model Building Interface☆51Updated 5 months ago
- Self-tuning HMC algorithms and evaluations☆19Updated 10 months ago
- Tools for an Aesara-based PPL.☆66Updated 9 months ago
- ☆12Updated 3 years ago
- Simulation based calibration and generation of synthetic data.☆55Updated 2 months ago
- Hidden Markov models in PyMC3☆99Updated last year
- PyVBMC: Variational Bayesian Monte Carlo algorithm for posterior and model inference in Python☆120Updated 6 months ago
- Bayesian learning and inference for state space models (SSMs) using Google Research's JAX as a backend☆61Updated last year
- An interactive visualization tool that transforms probabilistic programming models into an "Interactive Probabilistic Models Explorer".☆24Updated last year
- Powerful add-ons for PyMC☆112Updated last week
- Bayesian Regression Models in Pyro☆72Updated last year
- Bayesian inference and posterior analysis for Python☆45Updated last year
- State of the art inference for your bayesian models.☆221Updated 3 months ago
- An HMC/NUTS implementation in Aesara☆31Updated 2 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 4 years ago
- Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX☆100Updated 2 years ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆104Updated last year
- Kullback-Leibler projections for Bayesian model selection in Python☆38Updated this week
- A simple library to run variational inference on Stan models.☆32Updated 2 years ago
- Source repository for the online book Exploratory Analysis of Bayesian Models.☆24Updated last week
- AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.☆40Updated last year
- A system for Bayesian estimation of state space models using PyMC☆35Updated last month