Generalized rejection sampling schemes and applications in signal processing
نویسندگان
چکیده
منابع مشابه
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 very popular in signal processing over the last years. However, in many problems of practical interest these techniques demand procedures for sampling from probability distributions with non-standard forms, hence we are often brou...
متن کاملPerfect sampling: a review and applications to signal processing
In recent years, Markov chain Monte Carlo (MCMC) sampling methods have gained much popularity among researchers in signal processing. The Gibbs and the Metropolis–Hastings algorithms, which are the two most popular MCMC methods, have already been employed in resolving a wide variety of signal processing problems. A drawback of these algorithms is that in general, they cannot guarantee that the ...
متن کاملA generalized Fourier domain: Signal processing framework and applications
In this paper, a signal processing framework in a generalized Fourier domain (GFD) is introduced. In this newly proposed domain, a parametric form of control on the periodic repetitions that occur due to sampling in the reciprocal domain is possible, without the need to increase the sampling rate. This characteristic and the connections of the generalized Fourier transform to analyticity and to...
متن کاملAdaptive importance sampling in signal processing
In Bayesian signal processing, all the information about the unknowns of interest is contained in their posterior distributions. The unknowns can be parameters of a model, or a model and its parameters. In many important problems, these distributions are impossible to obtain in analytical form. An alternative is to generate their approximations by Monte Carlo-based methods like Markov chain Mon...
متن کاملGeneralized IFS for Signal Processing
Several methods have been proposed to estimate theHölder exponents of signals [1, 4]. In this paper, we propose a new approach, based on a generalization of iterated functions system (IFS), which is well adapted to irregular continuous 1D signals. We also use these generalized IFS to build parsimonious models of complex signals and to perform segmentation on them. This paper is organized as fol...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Signal Processing
سال: 2010
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2010.04.025