[PDF][PDF] A tutorial on Bayesian estimation and tracking techniques applicable to nonlinear and non-Gaussian processes
AJ Haug - MITRE Corporation, McLean, 2005 - apps.dtic.mil
Nonlinear filtering is the process of estimating and tracking the state of a nonlinear
stochastic system from non-Gaussian noisy observation data. In this technical memorandum …
stochastic system from non-Gaussian noisy observation data. In this technical memorandum …
[BOOK][B] Digital signal processing with Kernel methods
A realistic and comprehensive review of joint approaches to machine learning and signal
processing algorithms, with application to communications, multimedia, and biomedical …
processing algorithms, with application to communications, multimedia, and biomedical …
Transdimensional Markov chains: A decade of progress and future perspectives
SA Sisson - Journal of the American Statistical Association, 2005 - Taylor & Francis
The last 10 years have witnessed the development of sampling frameworks that permit the
construction of Markov chains that simultaneously traverse both parameter and model …
construction of Markov chains that simultaneously traverse both parameter and model …
Independent doubly adaptive rejection Metropolis sampling within Gibbs sampling
Bayesian methods have become very popular in signal processing lately, even though
performing exact Bayesian inference is often unfeasible due to the lack of analytical …
performing exact Bayesian inference is often unfeasible due to the lack of analytical …
Learning a multivariate Gaussian mixture model with the reversible jump MCMC algorithm
This paper is a contribution to the methodology of fully Bayesian inference in a multivariate
Gaussian mixture model using the reversible jump Markov chain Monte Carlo algorithm. To …
Gaussian mixture model using the reversible jump Markov chain Monte Carlo algorithm. To …
Particle filters for tracking an unknown number of sources
JR Larocque, JP Reilly, W Ng - IEEE Transactions on Signal …, 2002 - ieeexplore.ieee.org
This paper addresses the application of sequential importance sampling (SIS) schemes to
tracking directions of arrival (DOAs) of an unknown number of sources, using a passive …
tracking directions of arrival (DOAs) of an unknown number of sources, using a passive …
Generalized rejection sampling schemes and applications in signal processing
Bayesian methods and their implementations by means of sophisticated Monte Carlo
techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become …
techniques, such as Markov chain Monte Carlo (MCMC) and particle filters, have become …
Joint model selection and parameter estimation by population Monte Carlo simulation
M Hong, MF Bugallo, PM Djuric - IEEE Journal of Selected …, 2010 - ieeexplore.ieee.org
In this paper, we study the problem of joint model selection and parameter estimation under
the Bayesian framework. We propose to use the Population Monte Carlo (PMC) …
the Bayesian framework. We propose to use the Population Monte Carlo (PMC) …
Fully Bayesian Wideband Direction-of-Arrival Estimation and Detection via RJMCMC
We propose a fully Bayesian approach to wideband, or broadband, direction-of-arrival (DoA)
estimation and signal detection. Unlike previous works in wideband DoA estimation and …
estimation and signal detection. Unlike previous works in wideband DoA estimation and …
[PDF][PDF] On multiple try schemes and the Particle Metropolis-Hastings algorithm
ABSTRACT Markov Chain Monte Carlo (MCMC) algorithms and Sequential Monte Carlo
(SMC) methods (aka, particle filters) are well-known Monte Carlo methodologies, widely …
(SMC) methods (aka, particle filters) are well-known Monte Carlo methodologies, widely …