Abstract
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes as shown by means of numerical simulations.
Original language | English |
---|---|
Title of host publication | 25th European Signal Processing Conference, EUSIPCO 2017 |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 201-205 |
Number of pages | 5 |
Volume | 2017-January |
ISBN (Electronic) | 9780992862671 |
ISBN (Print) | 978-1-5386-0751-0 |
DOIs | |
Publication status | Published - 23 Oct 2017 |
Event | 25th European Signal Processing Conference, EUSIPCO 2017 - Kos, Greece Duration: 28 Aug 2017 → 2 Sept 2017 |
Conference
Conference | 25th European Signal Processing Conference, EUSIPCO 2017 |
---|---|
Country/Territory | Greece |
City | Kos |
Period | 28/08/17 → 2/09/17 |
Keywords / Materials (for Non-textual outputs)
- Bayesian inference
- Gaussian processes (GP)
- Importance Sampling
- Markov Chain Monte Carlo (MCMC)