OxfordML / bayesquad
Library for Bayesian Quadrature
☆31Updated 5 years ago
Alternatives and similar repositories for bayesquad:
Users that are interested in bayesquad are comparing it to the libraries listed below
- Code for efficiently sampling functions from GP(flow) posteriors☆69Updated 4 years ago
- Supplementary code for the NeurIPS 2020 paper "Matern Gaussian processes on Riemannian manifolds".☆28Updated 4 years ago
- Riemannian Convex Potential Maps☆67Updated last year
- Neural likelihood-free methods in PyTorch.☆39Updated 4 years ago
- Approximate Inference Turns Deep Networks into Gaussian Processes (dnn2gp)☆48Updated 5 years ago
- A variational method for fast, approximate inference for stochastic differential equations.☆43Updated 6 years ago
- Re-Examining Linear Embeddings for High-dimensional Bayesian Optimization☆41Updated 3 years ago
- ☆15Updated 2 years ago
- [NeurIPS 2020] Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters (AHGP)☆21Updated 4 years ago
- Library for Bayesian Neural Networks in PyTorch (first version as published in ProbProg2020)☆42Updated 3 years ago
- Non-stationary spectral mixture kernels implemented in GPflow☆28Updated 6 years ago
- ☆37Updated 4 years ago
- Light-weighted code for Orthogonal Additive Gaussian Processes☆41Updated 5 months ago
- Implementation of GPLVM and Bayesian GPLVM in pytorch/gpytorch☆15Updated 3 years ago
- Combining smooth constraint for building DAG with normalizing flow in order to replace autoregressive transformations while keeping tract…☆44Updated last year
- Implicit Deep Adaptive Design (iDAD): Policy-Based Experimental Design without Likelihoods☆17Updated 3 years ago
- Implementation of stochastic variational inference for differentially deep gaussian processes☆15Updated 6 years ago
- ☆30Updated 2 years ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆29Updated 3 years ago
- Code repo for "Function-Space Distributions over Kernels"☆31Updated 4 years ago
- Stochastic Gradient Langevin Dynamics for Bayesian learning☆30Updated 3 years ago
- Normalizing Flows using JAX☆82Updated last year
- PyTorch code of "Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows" (NeurIPS 2020)☆47Updated 4 years ago
- Code repo for "Kernel Interpolation for Scalable Online Gaussian Processes"☆57Updated 3 years ago
- Nonparametric Differential Equation Modeling☆53Updated 10 months ago
- Modular Gaussian Processes☆15Updated 3 years ago
- Minimal Gaussian process library in JAX with a simple (custom) approach to state management.☆11Updated last year
- Repository for DTU Special Course, focusing on Variational Inference using Normalizing Flows (VINF). Supervised by Michael Riis Andersen☆25Updated 4 years ago
- Library for Auto-Encoding Sequential Monte Carlo☆18Updated last year
- PyTorch implementation for "Probabilistic Circuits for Variational Inference in Discrete Graphical Models", NeurIPS 2020☆16Updated 3 years ago