vboost
Variational inference method
An open source software implementation of a black-box variational inference method to approximate intractable distributions.
code supplement for variational boosting (https://arxiv.org/abs/1611.06585)
11 stars
6 watching
5 forks
Language: Python
last commit: over 7 years ago
Linked from 1 awesome list
Related projects:
Repository | Description | Stars |
---|---|---|
| An approximate Bayesian inference method for fitting and evaluating complex computational models with limited resources. | 219 |
| An implementation of Black Box Variational Inference techniques in Python | 7 |
| A software package implementing an ensemble boosting method with gradient descent | 184 |
| An approach to modeling complex distributions by iteratively adding normalizing flow components and training with gradient boosting | 27 |
| A Python library implementing a machine learning boosting framework with probabilistic prediction capabilities | 1,663 |
| PyTorch implementation of Noisy Natural Gradient as Variational Inference for Bayesian Neural Networks | 121 |
| An optimization method for machine learning that uses subspace optimization techniques to improve the efficiency of stochastic learning algorithms | 0 |
| An implementation of Copula Variational Bayes inference using information geometry, allowing for efficient estimation of parameters in various machine learning algorithms and high-dimensional problems. | 51 |
| An implementation of personalized federated learning using variational Bayesian inference on the MNIST dataset | 52 |
| An MCMC sampler library for Bayesian estimation using Python | 331 |
| An introduction to Bayesian methods and probabilistic programming for software developers | 26,868 |
| A Python library providing approximate inference methods for Bayesian Hidden Markov Models and their extensions. | 549 |
| A Monte Carlo simulation of the NBA season leveraging dbt, duckdb and evidence.dev for data analytics and business intelligence | 461 |
| An implementation of online multi-label ranking boosting using VFDT as weak learners | 4 |
| Improving generalization in federated learning by seeking flat minima through optimization techniques | 82 |