Nonparametric Bayesian inference for the spectral density function of a random field

Biometrika ◽  
2009 ◽  
Vol 97 (1) ◽  
pp. 238-245 ◽  
Author(s):  
Y. Zheng ◽  
J. Zhu ◽  
A. Roy
Keyword(s):  
Bayesian Inference ◽  
Spectral Density ◽  
Random Field ◽  
Density Function ◽  
Spectral Density Function ◽  
Nonparametric Bayesian

ON THE SPECTRAL MEASURES OF SOME WEAKLY STATIONARY SEQUENCES INVOLVING RANDOMLY SPACED OBSERVATIONS

2012 ◽  
Vol 12 (01) ◽  
pp. 1150004
Author(s):  
RICHARD C. BRADLEY
Keyword(s):  
Spectral Density ◽  
Density Function ◽  
Spectral Density Function ◽  
Strong Mixing ◽  
Spectral Measures ◽  
Mixing Conditions ◽  
Stationary Sequences ◽  
Second Moments ◽  
The Central Limit Theorem ◽  
Complex Valued

In an earlier paper by the author, as part of a construction of a counterexample to the central limit theorem under certain strong mixing conditions, a formula is given that shows, for strictly stationary sequences with mean zero and finite second moments and a continuous spectral density function, how that spectral density function changes if the observations in that strictly stationary sequence are "randomly spread out" in a particular way, with independent "nonnegative geometric" numbers of zeros inserted in between. In this paper, that formula will be generalized to the class of weakly stationary, mean zero, complex-valued random sequences, with arbitrary spectral measure.

scholarly journals Characterization and Design of Functional Quasi-Random Nanostructured Materials Using Spectral Density Function

2017 ◽  
Vol 139 (7) ◽  
Author(s):  
Shuangcheng Yu ◽  
Yichi Zhang ◽  
Chen Wang ◽  
Won-kyu Lee ◽  
Biqin Dong ◽  
...  
Keyword(s):  
Spectral Density ◽  
Density Function ◽  
Design Methodology ◽  
Light Trapping ◽  
Spectral Density Function ◽  
Particle Type ◽  
Bottom Up ◽  
Design Representation ◽  
Material Systems ◽  
Channel Type

Quasi-random nanostructures are playing an increasingly important role in developing advanced material systems with various functionalities. Current development of functional quasi-random nanostructured material systems (NMSs) mainly follows a sequential strategy without considering the fabrication conditions in nanostructure optimization, which limits the feasibility of the optimized design for large-scale, parallel nanomanufacturing using bottom-up processes. We propose a novel design methodology for designing isotropic quasi-random NMSs that employs spectral density function (SDF) to concurrently optimize the nanostructure and design the corresponding nanomanufacturing conditions of a bottom-up process. Alternative to the well-known correlation functions for characterizing the structural correlation of NMSs, the SDF provides a convenient and informative design representation that maps processing–structure relation to enable fast explorations of optimal fabricable nanostructures and to exploit the stochastic nature of manufacturing processes. In this paper, we first introduce the SDF as a nondeterministic design representation for quasi-random NMSs, as an alternative to the two-point correlation function. Efficient reconstruction methods for quasi-random NMSs are developed for handling different morphologies, such as the channel-type and particle-type, in simulation-based microstructural design. The SDF-based computational design methodology is illustrated by the optimization of quasi-random light-trapping nanostructures in thin-film solar cells for both channel-type and particle-type NMSs. Finally, the concurrent design strategy is employed to optimize the quasi-random light-trapping structure manufactured via scalable wrinkle nanolithography process.

scholarly journals Spectral analysis of vibration in weakly non-linear systems

2000 ◽  
Vol 22 (3) ◽  
pp. 181-192
Author(s):  
Nguyen Tien Khiem
Keyword(s):  
Nonlinear Systems ◽  
Spectral Density ◽  
Density Function ◽  
Asymptotic Methods ◽  
Random Excitation ◽  
Spectral Density Function ◽  
Kolmogorov Equation ◽  
Spectral Structure ◽  
Random Load ◽  
Weakly Nonlinear

The weakly nonlinear systems subjected to deterministic excitations have been fully and deeply studied by use of the well developed asymptotic methods [1-4]. The systems excited by a random load have been investigated mostly using the Fokker-Plank-Kolmogorov equation technique combined with the asymptotic methods [5-8]. However, the last approach in most successful cases allows to obtain only a stationary single point probability density function, that contains no information about the correlation nor' consequently, the spectral structure of the response. The linearization technique [9, 10] in general permits the spectral density of the response to be determined, but the spectral function obtained by this method because of the linearization eliminates the effect of the nonlinearity. Thus, spectral structure of response of weakly nonlinear systems to random excitation, to the author's knowledge, has not been studied enough. This paper deals with the above mentioned problem. The main idea of this work is the use of an analytical simulation of random excitation given by its spectral density function and afterward application of the well known procedure of the asymptotic method to obtain an asymptotic expression of the response spectral density function. The obtained spectral relationship covers the linear system case and especially emphasizes the nonlinear effect on the spectral density of response. The theory will be illustrated by an example and at the end of this paper there will be a discussion about the obtained results.  

scholarly journals Spectral-Density Function in the System of Randomly Distributed Spins withR-nInteractions

1972 ◽  
Vol 48 (6) ◽  
pp. 1769-1792 ◽  
Author(s):  
Juichiro Hama ◽  
Teruo Inuzuka ◽  
Tuto Nakamura
Keyword(s):  
Spectral Density ◽  
Density Function ◽  
Spectral Density Function

scholarly journals Best Lag Window for Spectrum Estimation of Law Order MA Process

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Ali Sami Rashid ◽  
Mohammed Jabber Hawas Allami ◽  
Ahmed Kareem Mutasher
Keyword(s):  
Computer Simulation ◽  
Spectral Density ◽  
Density Function ◽  
Moving Average ◽  
Main Tool ◽  
Spectral Density Function ◽  
Spectrum Estimation ◽  
Ma Process ◽  
Special Case

In this article, we investigate spectrum estimation of law order moving average (MA) process. The main tool is the lag window which is one of the important components of the consistent form to estimate spectral density function (SDF). We show, based on a computer simulation, that the Blackman window is the best lag window to estimate the SDF of MA1 and MA2 at the most values of parameters βi and series sizes n, except for a special case when β=−1 and n≥40 in MA1. In addition, the Hanning–Poisson window appears as the best to estimate the SDF of MA2 when β1=β2=−0.5 and n≥40.

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