# Parallel Tempering via Simulated Tempering Without Normalizing Constants

## TL;DR

This paper introduces a novel Bayesian sampling method that enhances Simulated Tempering by eliminating the need for normalizing constants, allowing for continuous temperature schedules and efficient joint parameter and marginal likelihood estimation.

## Contribution

It develops a new Bayesian methodology that modifies Simulated Tempering to avoid calculating normalizing constants, enabling continuous temperature schedules and improved efficiency.

## Key findings

- Effective in mixture models of Gaussian distributions
- Applicable to ordinary differential equation models
- Provides accurate marginal likelihood estimates

## Abstract

In this paper we develop a new general Bayesian methodology that simultaneously estimates parameters of interest and the marginal likelihood of the model. The proposed methodology builds on Simulated Tempering, which is a powerful algorithm that enables sampling from multi-modal distributions. However, Simulated Tempering comes with the practical limitation of needing to specify a prior for the temperature along a chosen discretization schedule that will allow calculation of normalizing constants at each temperature. Our proposed model defines the prior for the temperature so as to remove the need for calculating normalizing constants at each temperature and thereby enables a continuous temperature schedule, while preserving the sampling efficiency of the Simulated Tempering algorithm. The resulting algorithm simultaneously estimates parameters while estimating marginal likelihoods through thermodynamic integration. We illustrate the applicability of the new algorithm to different examples involving mixture models of Gaussian distributions and ordinary differential equation models.