darrenjw / fp-ssc-courseLinks
An introduction to functional programming for scalable statistical computing
☆76Updated last year
Alternatives and similar repositories for fp-ssc-course
Users that are interested in fp-ssc-course are comparing it to the libraries listed below
Sorting:
- Tutorials and sampling algorithm comparisons☆77Updated this week
- A Simple Statistical Distribution Library in JAX☆16Updated last year
- probabilistic programming focused on fun☆43Updated this week
- Tools for an Aesara-based PPL.☆66Updated 11 months ago
- State of the art inference for your bayesian models.☆226Updated 5 months ago
- Exponential families for JAX☆75Updated last week
- Inference Combinators in JAX☆51Updated 5 months ago
- AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.☆38Updated 2 years ago
- Evaluation Framework for Probabilistic Programming Languages☆103Updated last year
- Structural Time Series in JAX☆200Updated last year
- Oryx is a library for probabilistic programming and deep learning built on top of Jax.☆280Updated 2 weeks ago
- Jax SSM Library☆48Updated 2 years ago
- Bayesian inference and posterior analysis for Python☆46Updated last year
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆105Updated 2 years 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
- Tools for JAX☆49Updated last week
- A generic interface for linear algebra backends☆74Updated 7 months ago
- Efficient, lightweight variational inference and approximation bounds☆45Updated last week
- Documentation:☆124Updated 2 years ago
- Stencil computations in JAX☆71Updated 2 years ago
- Minimal Implementation of Bayesian Optimization in JAX☆98Updated 6 months ago
- Composable kernels for scikit-learn implemented in JAX.☆45Updated 5 years ago
- Bayesian optimization of discrete sequences☆21Updated 3 years ago
- Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.☆239Updated last year
- Visualize, create, and operate on pytrees in the most intuitive way possible.☆45Updated 9 months ago
- Functional tensors for probabilistic programming☆243Updated 2 years ago
- Lectures on Quantitative Economics Using JAX☆45Updated 3 weeks ago
- ☆247Updated 3 months ago
- ☆12Updated 3 years ago