Mu-DS / practical_training
Material for the MUDS Practical Data Science Training
☆14Updated 4 years ago
Alternatives and similar repositories for practical_training:
Users that are interested in practical_training are comparing it to the libraries listed below
- Robust initialisation of inducing points in sparse variational GP regression models.☆33Updated 2 years ago
- Bayesian Uncertainty Quantification by Deep Generative Model☆20Updated 4 years ago
- Diagnostics for Conditional Density Estimators and Bayesian Inference Algorithms☆14Updated 3 years ago
- SBI Workshop jointly by Helmholtz AI + ML ⇌ Science Colaboratory☆23Updated last year
- Sliced Iterative Generator (SIG) & Gaussianizing Iterative Slicing (GIS)☆36Updated 2 years ago
- Probabilistic modeling of tabular data with normalizing flows.☆55Updated last month
- Density Estimation Likelihood-Free Inference with neural density estimators and adaptive acquisition of simulations☆108Updated last year
- Likelihood-free AMortized Posterior Estimation with PyTorch☆126Updated 7 months ago
- Lightweight library of stochastic gradient MCMC algorithms written in JAX.☆102Updated last year
- Deep GPs built on top of TensorFlow/Keras and GPflow☆124Updated 5 months ago
- A miscellaneous set of helper functions, custom distributions, and other utilities that I find useful when using NumPyro in my work☆24Updated 2 months ago
- Combination of transformers and diffusion models for flexible all-in-one simulation-based inference☆61Updated 9 months ago
- A list of Python-based MCMC & ABC packages☆123Updated 9 months ago
- Probabilistic Programming and Nested sampling in JAX☆172Updated 3 months ago
- Supplementary code for the AISTATS 2021 paper "Matern Gaussian Processes on Graphs".☆53Updated 2 months ago
- Normalizing flow models allowing for a conditioning context, implemented using Jax, Flax, and Distrax.☆18Updated last year
- Conditional density estimation with neural networks☆30Updated 2 months ago
- MCHMC: sampler from an arbitrary differentiable distribution☆71Updated last month
- A Python package for approximate Bayesian inference and optimization using Gaussian processes☆42Updated last year
- Simulation-based inference benchmark☆96Updated 2 months ago
- Simulation-based inference in JAX☆31Updated last month
- Coverage tests to check the quality of your posterior estimators.☆30Updated 7 months ago
- PhD Thesis☆31Updated last year
- ABCpy package☆114Updated 10 months ago
- Numerical quadrature with JAX☆53Updated last week
- Lightweight MCMC sampling for PyTorch Models aka My Corona Project☆45Updated 4 years ago
- A high-dimensional Kolmogorov-Smirnov distance for comparing high dimensional distributions☆26Updated 2 years ago