dilinwang820 / Stein-Variational-Gradient-Descent
code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"
☆391Updated 10 months ago
Alternatives and similar repositories for Stein-Variational-Gradient-Descent:
Users that are interested in Stein-Variational-Gradient-Descent are comparing it to the libraries listed below
- Deep neural network kernel for Gaussian process☆203Updated 4 years ago
- Deep Gaussian Processes in Python☆232Updated 3 years ago
- Convolutional Gaussian processes based on GPflow.☆96Updated 7 years ago
- A community repository for benchmarking Bayesian methods☆109Updated 3 years ago
- The collection of papers about combining deep learning and Bayesian nonparametrics☆120Updated 5 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆148Updated 5 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆105Updated 7 years ago
- I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth☆181Updated 10 years ago
- Python package facilitating the use of Bayesian Deep Learning methods with Variational Inference for PyTorch☆358Updated 5 years ago
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆98Updated 5 years ago
- Gaussian Processes in Pytorch☆75Updated 4 years ago
- Demos demonstrating the difference between homoscedastic and heteroscedastic regression with dropout uncertainty.☆140Updated 8 years ago
- Reimplementation of Variational Inference with Normalizing Flows (https://arxiv.org/abs/1505.05770)☆231Updated 6 years ago
- Code for "A-NICE-MC: Adversarial Training for MCMC"☆126Updated 6 years ago
- Sample code for running deterministic variational inference to train Bayesian neural networks☆100Updated 6 years ago
- Understanding normalizing flows☆131Updated 5 years ago
- Structured Inference Networks for Nonlinear State Space Models☆267Updated 7 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆249Updated 6 months ago
- code for Structured Variational Autoencoders☆351Updated 6 years ago
- pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.☆214Updated 6 years ago
- Manifold Markov chain Monte Carlo methods in Python☆226Updated 3 weeks ago
- Papers for Bayesian-NN☆318Updated 5 years ago
- Streaming sparse Gaussian process approximations☆62Updated 2 years ago
- Probabilistic Torch is library for deep generative models that extends PyTorch☆887Updated 9 months ago
- A probabilistic programming system for simulators and high-performance computing (HPC), based on PyTorch☆392Updated 9 months ago
- Package implementing various parametric and nonparametric methods for conditional density estimation☆191Updated last year
- Max-value Entropy Search for Efficient Bayesian Optimization☆71Updated 2 years ago
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆81Updated 4 years ago
- Dropout As A Bayesian Approximation: Code☆201Updated 9 years ago
- Heterogeneous Multi-output Gaussian Processes☆51Updated 4 years ago