# Mixture Models for the Analysis, Edition, and Synthesis of Continuous Time Series

arXiv: https://arxiv.org/abs/2104.10731 · HTML: https://mlsanity.com/paper/2104.10731/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 34 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Kernelized Movement Primitives](https://mlsanity.com/paper/1708.08638/) (2017)
- [Ergodic Exploration of Distributed Information](https://mlsanity.com/paper/1708.09352/) (2017)
- [Bayesian Gaussian mixture model for robotic policy imitation](https://mlsanity.com/paper/1904.10716/) (2019)
- [Small Variance Asymptotics for Non-Parametric Online Robot Learning](https://mlsanity.com/paper/1610.02468/) (2016)
- [Ergodic Exploration using Binary Sensing for Non-Parametric Shape Estimation](https://mlsanity.com/paper/1709.01560/) (2017)

## Built on by (most cited first, 2 of 2)

- [Conditional Neural Expert Processes for Learning Movement Primitives from Demonstration](https://mlsanity.com/paper/2402.08424/) (2024)
- [Tensor Train for Global Optimization Problems in Robotics](https://mlsanity.com/paper/2206.05077/) (2022)

## Abstract (arXiv)

This chapter presents an overview of techniques used for the analysis, edition, and synthesis of time series, with a particular emphasis on motion data. The use of mixture models allows the decomposition of time signals as a superposition of basis functions. It provides a compact representation that aims at keeping the essential characteristics of the signals. Various types of basis functions have been proposed, with developments originating from different fields of research, including computer graphics, human motion science, robotics, control, and neuroscience. Examples of applications with radial, Bernstein and Fourier basis functions will be presented, with associated source codes to get familiar with these techniques.