https://github.com/modestbayes/hamiltonian

Speed up HMC with neural network gradient approximation
https://github.com/modestbayes/hamiltonian

Last synced: 14 days ago
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Speed up HMC with neural network gradient approximation

• Host: GitHub
• URL: https://github.com/modestbayes/hamiltonian
• Owner: modestbayes
• Created: 2017-05-10T01:30:13.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2017-11-07T01:42:16.000Z (about 7 years ago)
• Last Synced: 2024-08-01T16:48:08.718Z (3 months ago)
• Language: Jupyter Notebook
• Homepage:
• Size: 227 KB
• Stars: 8
• Watchers: 3
• Forks: 5
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

        
# Neural network gradient hamiltonian monte carlo

Hamiltonian monte carlo (HMC) is an effective way to sample from posterior distirbutions. HMC explores the parameter space by following the gradient field. We can speed up HMC with neural network gradient approximation when data are abundant.

## Sampler

HMC sampler

## Surrogate

* Shallow neural network

* Gradient neural network

* Gaussian process

* Stochastic gradient

## Models

* Multivariate Gaussian

* Logistic regression with Normal prior

* Logistic regression with Laplace prior