# Inference with Hamiltonian Sequential Monte Carlo Simulators

## TL;DR

This paper introduces a novel Monte Carlo simulation method that integrates Sequential Monte Carlo and Hamiltonian Monte Carlo techniques, enhancing robustness in complex, multimodal inference problems.

## Contribution

The paper presents a new hybrid Monte Carlo simulator that leverages the strengths of both SMC and HMC, addressing limitations in existing inference methods.

## Key findings

- Effective in handling multimodal distributions
- Robust to complex likelihood functions
- Demonstrated through multiple examples

## Abstract

The paper proposes a new Monte-Carlo simulator combining the advantages of Sequential Monte Carlo simulators and Hamiltonian Monte Carlo simulators. The result is a method that is robust to multimodality and complex shapes to use for inference in presence of difficult likelihoods or target functions. Several examples are provided.