NDLI: Efficient Sequential Monte-Carlo<?Pub _newline ?> Samplers for Bayesian Inference

Content Provider	IEEE Xplore Digital Library
Author	Thi Le Thu Nguyen Septier, F. Peters, G.W. Delignon, Y.
Copyright Year	1991
Abstract	In many problems, complex non-Gaussian and/or nonlinear models are required to accurately describe a physical system of interest. In such cases, Monte-Carlo algorithms are remarkably flexible and extremely powerful approaches to solve such inference problems. However, in the presence of a high-dimensional and/or multimodal posterior distribution, it is widely documented that standard Monte-Carlo techniques could lead to poor performance. In this paper, the study is focused on a Sequential Monte-Carlo (SMC) sampler framework, a more robust and efficient Monte-Carlo algorithm. Although this approach presents many advantages over traditional Monte-Carlo methods, the potential of this emergent technique is, however, largely underexploited in signal processing. In this paper, we aim at proposing some novel strategies to improve the efficiency and facilitate practical implementation of the SMC sampler. First, we propose an automatic and adaptive strategy that selects the sequence of distributions within the SMC sampler that minimizes the asymptotic variance of the estimator of the posterior normalization constant. The second original contribution we present improves the global efficiency of the SMC sampler by introducing a novel correction mechanism that allows the use of the particles generated through all of the iterations of the algorithm (instead of only particles from the last iteration). This is a significant contribution as it removes the need to discard a large portion of the samples obtained, as is standard in standard SMC methods. This will improve estimation performance in practical settings where the computational budget is important to consider.
Sponsorship	IEEE Signal Processing Society
Starting Page	1305
Ending Page	1319
Page Count	15
File Size	3439346
File Format	PDF
ISSN	1053587X
Volume Number	64
Issue Number	5
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Monte Carlo methods Signal processing algorithms Standards Kernel Bayes methods Markov processes Signal processing sequential Monte Carlo sampler Bayesian inference complex models
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces

Annealed SMC Samplers for Nonparametric Bayesian Mixture Models

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

Joint Bayesian Decomposition of a Spectroscopic Signal Sequence

Electrocardiogram Signal Modeling With Adaptive Parameter Estimation Using Sequential Bayesian Methods

A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

Batch and Sequential Bayesian Estimators of the Number of Active Terminals in an IEEE 802.11 Network

Nonparametric Bayesian modeling of complex networks: an introduction

A Deterministic Sequential Monte Carlo Method for Haplotype Inference

Efficient Sequential Monte-Carlo Samplers for Bayesian Inference

Similar Documents

Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces

Annealed SMC Samplers for Nonparametric Bayesian Mixture Models

A Bayesian Perspective on Multiple Source Localization in Wireless Sensor Networks

Joint Bayesian Decomposition of a Spectroscopic Signal Sequence

Electrocardiogram Signal Modeling With Adaptive Parameter Estimation Using Sequential Bayesian Methods

A Survey of Stochastic Simulation and Optimization Methods in Signal Processing

Batch and Sequential Bayesian Estimators of the Number of Active Terminals in an IEEE 802.11 Network

Nonparametric Bayesian modeling of complex networks: an introduction

A Deterministic Sequential Monte Carlo Method for Haplotype Inference

Efficient Sequential Monte-Carlo Samplers for Bayesian Inference