PredictiveScienceLab / pysmcLinks
Sequential Monte Carlo working on top of pymc
☆50Updated 6 years ago
Alternatives and similar repositories for pysmc
Users that are interested in pysmc are comparing it to the libraries listed below
Sorting:
- Kernel structure discovery research code - likely to be unstable☆191Updated 10 years ago
- ELFI - Engine for Likelihood-Free Inference☆277Updated 3 months ago
- MCMC samplers for Bayesian estimation in Python, including Metropolis-Hastings, NUTS, and Slice☆339Updated 2 years ago
- Likelihood-free inference toolbox.☆56Updated 8 years ago
- Collection of jupyter notebooks for demonstrating software.☆168Updated 2 years ago
- ☆237Updated 8 years ago
- Additional kernels that can be used with scikit-learn's Gaussian Process module☆82Updated last year
- I am in [research] stepped in so far that, should I wade no more, Returning were as tedious as go o'er. -MacBeth☆185Updated 11 years ago
- Deep Gaussian Processes in matlab☆93Updated 3 years ago
- pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.☆216Updated 6 years ago
- Parameterization Framework for parameterized model creation and handling.☆49Updated last month
- Just a little MCMC☆227Updated last year
- Bayesian optimization in high-dimensions via random embedding.☆114Updated 12 years ago
- Experiment code for Stochastic Gradient Hamiltonian Monte Carlo☆106Updated 7 years ago
- A probabilistic programming framework based on TensorFlow☆87Updated 6 years ago
- python version of the No-U-Turn Sampler (NUTS) from Hoffman & Gelman, 2011☆132Updated 4 years ago
- Python package for modular Bayesian optimization☆136Updated 4 years ago
- Python library for optimizing noisy functions.☆88Updated 3 years ago
- Fast and flexible Gaussian Process regression in Python☆460Updated last week
- Code for AutoGP☆27Updated 6 years ago
- Code to produce demos of Metroplis-Hastings and Hamiltonian Monte Carlo samplers.☆36Updated 11 years ago
- Bayesian Optimization using GPflow☆271Updated 4 years ago
- TensorFlow implementation for training MCMC samplers from the paper: Generalizing Hamiltonian Monte Carlo with Neural Network☆183Updated 6 years ago
- Modular Probabilistic Programming on MXNet☆104Updated 2 years ago
- Experimental code for porting PyMC to alternative backends☆26Updated 7 years ago
- Keras + Gaussian Processes: Learning scalable deep and recurrent kernels.☆249Updated last year
- Hamiltonain Monte Carlo in Python☆40Updated 5 years ago
- code for the paper "Stein Variational Gradient Descent (SVGD): A General Purpose Bayesian Inference Algorithm"☆405Updated last year
- Implementation in C and Theano of the method Probabilistic Backpropagation for scalable Bayesian inference in deep neural networks.☆192Updated 6 years ago
- Optimizers for machine learning☆183Updated last year