naesseth / nestedsmc
Implementation of various algorithms in the Nested Sequential Monte Carlo family of methods.
☆14Updated 9 years ago
Related projects ⓘ
Alternatives and complementary repositories for nestedsmc
- ☆12Updated 6 years ago
- Stochastic Gradient Riemannian Langevin Dynamics☆32Updated 9 years ago
- Python package for inference with Gaussian processes☆11Updated 9 years ago
- Reducing Reparameterization Gradient Variance code.☆33Updated 7 years ago
- This repository houses the code for the community website http://www.probabilistic-numerics.org☆35Updated 4 years ago
- ☆12Updated last year
- Optimally-weighted herding is Bayesian Quadrature☆16Updated 8 years ago
- Repo for a paper about constructing priors on very deep models.☆70Updated 8 years ago
- ☆11Updated 8 years ago
- Conditional trees☆12Updated 11 years ago
- Fastidious accounting of entropy streams into and out of optimization and sampling algorithms.☆31Updated 8 years ago
- Implementation of an algorithm for Markov chain Monte Carlo with data subsampling☆32Updated 8 years ago
- Source code and data for the tutorial: "Getting started with particle Metropolis-Hastings for inference in nonlinear models"☆25Updated 5 years ago
- Scalable Log Determinants for Gaussian Process Kernel Learning (https://arxiv.org/abs/1711.03481) (NIPS 2017)☆18Updated 7 years ago
- "Discontinuous Hamiltonian Monte Carlo for sampling discrete parameters" by Akihiko Nishimura, David Dunson, Jianfeng Lu☆27Updated 6 years ago
- Notes from Simons Institute program "Foundations of Machine Learning"☆13Updated 7 years ago
- Look Ahead Hamiltonian Monte Carlo☆30Updated 9 years ago
- Software for learning sparse Bayesian networks☆43Updated 4 years ago
- Code for density estimation with nonparametric cluster shapes.☆38Updated 8 years ago
- Automatic Reparameterisation of Probabilistic Programs☆36Updated 4 years ago
- Code for AutoGP☆26Updated 5 years ago
- Code for Kernel Adaptive Metropolis-Hastings☆33Updated 9 years ago
- Code for NIPS 2015 "Gradient-Free Hamiltonian Monte Carlo via Effecient Kernel Exponential Families"☆24Updated 6 years ago
- Source for experiments in the Additive Gaussian process paper, as well as extensions relating to dropout.☆21Updated 10 years ago
- an implementation of latent Dirichlet allocation (LDA) with stochastic variational inference☆20Updated 7 years ago
- Code for "Efficient optimization of loops and limits with randomized telescoping sums"☆27Updated 5 years ago
- Implementation of Hamiltonian Monte Carlo using Google's TensorFlow☆48Updated 8 years ago
- This is code associated with the paper: Broderick, T, Boyd, N, Wibisono, A, Wilson, AC, and Jordan, MI. Streaming variational Bayes. Neur…☆41Updated 10 years ago
- The Mondrian kernel is a random feature approximation to the Laplace kernel allowing fast kernel width selection.☆12Updated 8 years ago
- An implementation of "Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles" (http://arxiv.org/abs/1612.01474)☆34Updated 7 years ago