# Parallel-tempered Stochastic Gradient Hamiltonian Monte Carlo for Approximate Multimodal Posterior Sampling

## TL;DR

This paper introduces a novel sampler combining parallel tempering with Nosé-Hoover dynamics to efficiently sample from complex, multimodal posterior distributions in large-scale Bayesian learning tasks.

## Contribution

The paper presents a new stochastic gradient Hamiltonian Monte Carlo method that integrates parallel tempering and Nosé-Hoover dynamics for improved multimodal posterior sampling.

## Key findings

- Effectively samples from complex multimodal distributions.
- Handles noisy stochastic gradients in large datasets.
- Facilitates deep Bayesian learning with complex posteriors.

## Abstract

We propose a new sampler that integrates the protocol of parallel tempering with the Nos\'e-Hoover (NH) dynamics. The proposed method can efficiently draw representative samples from complex posterior distributions with multiple isolated modes in the presence of noise arising from stochastic gradient. It potentially facilitates deep Bayesian learning on large datasets where complex multimodal posteriors and mini-batch gradient are encountered.