eth-cscs / abcpy-models
☆12Updated 2 years ago
Alternatives and similar repositories for abcpy-models:
Users that are interested in abcpy-models are comparing it to the libraries listed below
- This tutorial is a basic guide to understanding the Zig-Zag Sampling method. This document is released with the aim of diffusion and shar…☆19Updated 5 years ago
- An ultra-lightweight JAX implementation of sparse Gaussian processes via pathwise sampling.☆22Updated 3 years ago
- Fully Bayesian Inference in GPs - Gaussian and Generic Likelihoods☆22Updated last year
- Kernels, the machine learning ones☆14Updated last year
- ☆36Updated 3 years ago
- probabilistic programming focused on fun☆39Updated 3 months ago
- A simple library to run variational inference on Stan models.☆30Updated last year
- A variational method for fast, approximate inference for stochastic differential equations.☆43Updated 6 years ago
- A Julia implementation of sparse Gaussian processes via path-wise doubly stochastic variational inference.☆33Updated 4 years ago
- Bayesian inference and posterior analysis for Python☆44Updated last year
- Gaussian-Processes Surrogate Optimisation in python☆19Updated 3 years ago
- R package to perform statistical inference in generative models using the Wasserstein distance (requires CGAL)☆25Updated 4 years ago
- The official implementation of Non-separable Spatio-temporal Graph Kernels via SPDEs.☆16Updated 2 years ago
- Compares Stan, PyMC, and PyMC + JAX numpyro sampler on a model for tennis☆31Updated 2 years ago
- Benchmarks of Bayesian Nonparametric models in Turing and other PPLs☆29Updated 6 months ago
- Bayesian inference for a logistic regression model in various languages☆43Updated last year
- stan2tfp is a lightweight interface to the TensorFlow Probability backend of the Stan compiler. It provides the necessary objects and fun…☆24Updated 3 years ago
- DifferentialEquations.jl with PyTorch☆11Updated 2 years ago
- Practical tools for quantifying how well a sample approximates a target distribution☆27Updated 4 years ago
- Initial look at directed acyclic graph (DAG) based causal models in regression.☆31Updated 10 months ago
- Gaussian Process and Uncertainty Quantification Summer School 2020☆33Updated 2 years ago
- Painless optimisation of constrained variables in AutoGrad, TensorFlow, PyTorch, and JAX☆23Updated last year
- Dynamic mode decomposition with dependent structure among observables (Graph DMD)☆11Updated 5 years ago
- A Simple Statistical Distribution Library in JAX☆16Updated 10 months ago
- Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"☆28Updated 5 years ago
- Implementation of the Gaussian Process Autoregressive Regression Model☆62Updated last week
- Turing Workshop☆14Updated last year
- Course material for the PhD course in Advanced Bayesian Learning☆59Updated 2 months ago
- ☆30Updated 2 years ago
- Lectures on Quantitative Economics Using JAX☆31Updated last month