yao-yl / Evaluating-Variational-InferenceLinks
Evaluating variational inference using Pareto-smoothed importance sampling and simulation-based calibration
☆12Updated 7 years ago
Alternatives and similar repositories for Evaluating-Variational-Inference
Users that are interested in Evaluating-Variational-Inference are comparing it to the libraries listed below
Sorting:
- Efficient, lightweight variational inference and approximation bounds☆42Updated last year
- Variational inference for Bayesian logistic regression☆12Updated 5 years ago
- Unbiased Markov chain Monte Carlo with couplings☆29Updated 2 years ago
- OxCSML research group reading groups and meetings at the Department of Statistics, University of Oxford.☆93Updated 3 years ago
- Deep Gaussian Processes with Doubly Stochastic Variational Inference☆150Updated 6 years ago
- Reference implementation of variational sequential Monte Carlo proposed by Naesseth et al. "Variational Sequential Monte Carlo" (2018)☆65Updated 6 years ago
- A community repository for benchmarking Bayesian methods☆110Updated 3 years ago
- some scripts for the couplings enthusiasts!☆32Updated 5 years ago
- mean-field and structured VAEs for the IBP☆23Updated 7 years ago
- Pareto smoothed importance sampling (PSIS) and PSIS leave-one-out cross-validation for Python and Matlab/Octave☆77Updated last year
- ☆22Updated last year
- Light-weighted code for Orthogonal Additive Gaussian Processes☆43Updated last year
- Code for "Functional variational Bayesian neural networks" (https://arxiv.org/abs/1903.05779)☆85Updated 5 years ago
- Black Box Variational Inference☆14Updated 10 years ago
- sgmcmc: a stochastic gradient MCMC package for R☆29Updated 4 years ago
- Computational statistics and machine learning reading group at Imperial College London (2019-2020)☆24Updated 5 months ago
- A simple library to run variational inference on Stan models.☆32Updated 2 years ago
- Tools for conformal inference in regression☆248Updated 11 months ago
- Gaussian Processes for Sequential Data☆18Updated 4 years ago
- Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design☆35Updated 4 years ago
- ☆40Updated 6 years ago
- Bayesian Deep Learning with Stochastic Gradient MCMC Methods☆39Updated 3 years ago
- Extensible Tensorflow library for differentiable particle filtering. ICML 2021.☆42Updated 2 years ago
- Automated Scalable Bayesian Inference☆131Updated 3 years ago
- General Latent Feature Modeling for Heterogeneous data☆49Updated last year
- Fast C code for sampling Polya-gamma random variates. Builds on Jesse Windle's BayesLogit library.☆82Updated 5 years ago
- Sparse Orthogonal Variational Inference for Gaussian Processes (SOLVE-GP)☆22Updated 4 years ago
- Code for the Bayesian Synthetic Likelihood paper by Price et al 2018 in the Journal of Computational and Graphical Statistics (volume 27,…☆14Updated 7 years ago
- Experiments of amortized stein variational gradient☆16Updated 8 years ago
- Convolutional Gaussian processes based on GPflow.☆95Updated 7 years ago