Line spectrum estimation with probabilistic priors
نویسندگان
چکیده
منابع مشابه
Line Spectrum Estimation with Probabilistic Priors
For line spectrum estimation, we derive the maximum a posteriori probability estimator where prior knowledge of frequencies is modeled probabilistically. Since the spectrum is periodic, an appropriate distribution is the circular von Mises distribution that can parameterize the entire range of prior certainty of the frequencies. An efficient alternating projections method is used to solve the r...
متن کاملProbabilistic Abduction without Priors
This paper considers the simple problem of abduction in the framework of Bayes theorem, when the prior probability of the hypothesis is not available, either because there are no statistical data on which to rely on, or simply because a human expert is reluctant to provide a subjective assessment of this prior probability. The problem remains an open issue since a simple sensitivity analysis on...
متن کاملExtend Transferable Belief Models with Probabilistic Priors
In this paper, we extend Smets’ transferable belief model (TBM) with probabilistic priors. Our first motivation for the extension is about evidential reasoning when the underlying prior knowledge base is Bayesian. We extend standard Dempster models with prior probabilities to represent beliefs and distinguish between two types of induced mass functions on an extended Dempster model: one for bel...
متن کاملVariational fluid motion estimation with physical priors
In this thesis, techniques for Particle Image Velocimetry (PIV) and Particle Tracking Velocimetry (PTV) are developed that are based on variational methods. The basic idea is not to estimate displacement vectors locally and individually, but to estimate vector fields as a whole by minimizing a suitable functional defined over the entire image domain (which may be 2D or 3D and may also include t...
متن کاملReasoning about Deterministic Action Sequences with Probabilistic Priors
We present a novel algorithm and a new understanding of reasoning about a sequence of deterministic actions with a probabilistic prior. When the initial state of a dynamic system is unknown, a probability distribution can be still specified over the initial states. Estimating the posterior distribution over states (filtering) after some deterministic actions occurred is a problem relevant to AI...
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: 2013
ISSN: 0165-1684
DOI: 10.1016/j.sigpro.2013.03.038