On Bayesian Nonparametric Continuous Time Series Models

TL;DR

This paper discusses Bayesian nonparametric mixture models for continuous time series, highlighting a key requirement and identifying a well-known model that satisfies it, relevant to multiple change-point problems.

Contribution

It reveals a specific Bayesian nonparametric model that meets a crucial requirement for continuous time series analysis, connecting it to change-point detection.

Findings

Identifies a key requirement for Bayesian nonparametric models in continuous time series.

Establishes that a known model satisfies this requirement.

Links the model to multiple change-point problems.

Abstract

This paper is a note on the use of Bayesian nonparametric mixture models for continuous time series. We identify a key requirement for such models, and then establish that there is a single type of model which meets this requirement. As it turns out, the model is well known in multiple change-point problems.