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Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized...
In this paper, we apply probability density function (PDF) projection to arrive at an exact closed-form expression for the marginal distribution of the visible data of a restricted Boltzmann machine (RBM) without requiring integrating over the distribution of the hidden variables or needing to know the partition function. We express the visible data marginal as a projected PDF based on a set of sufficient...
This paper addresses the problem of multichannel audio source separation in under-determined convolutive mixtures. We target a semi-blind scenario assuming that the mixing filters are known. The convolutive mixing process is exactly modeled using the time-domain impulse responses of the mixing filters. We propose a Student's t time-frequency source model based on non-negative matrix factorization...
In this paper, a generalization of the Misspecified Cramér-Rao Bound (MCRB) and of the Constrained MCRB (CMCRB) to complex parameter vectors is presented. Our derivation aims at providing lower bounds on the Mean Square Error (MSE) for both circular and non-circular, MS-unbiased, mismatched estimators. A simple toy example is also presented to clarify the theoretical findings.
Jitter is a critical factor to the performance of highspeed signal links. Jitter can be modeled as a random process. Both the probability density function (PDF) and the spectral characteristics of the jitter are important for evaluating the impact to the channel performance. The concept of numerical conditional probability density function (NCPDF) and a new statistical method called FastBER are proposed...
Statistical link analysis and link budget calculation is the essential part for current high-speed system design. Because of the difficulty of low bit-error rate (BER) simulation at SPICE transient solver, several methods were proposed to simulate low BER in a short time. Single-bit response (SBR) method is the most basic method; it uses SBR to construct the arbitrary waveform under the assumption...
We investigate the estimation of normalisation constants of probability distributions using nonlinear importance sampling (IS). This is a problem that involves the solution of complicated multidimensional integrals and, in general, does not admit a closed-form solution or approximation. It is especially relevant for Bayesian model assessment problems, where the normalisation constant of the posterior...
Monte Carlo (MC) methods are widely used for Bayesian inference in signal processing, machine learning and statistics. In this work, we introduce an adaptive importance sampler which mixes together the benefits of the Importance Sampling (IS) and Markov Chain Monte Carlo (MCMC) approaches. Different parallel MCMC chains provide the location parameters of the proposal probability density functions...
Cognitive radio (CR) has been identified as an enabling technology toward meeting the high spectrum utilization efficiency demand in future internet-of-things (IoT) systems. Development of new spectrum sensing schemes better suited to CR-based IoT networks, which are typically heterogeneous with perfect network-wide synchronization difficult to achieve, is thus rather crucial. Motivated by the low-complexity...
Structural Health Monitoring utilizes sensor network system embedded within structure to evaluate the size and location of structural damage, and perform the remaining useful life prediction and reliability assessment. Piezoelectric (PZT) and intelligent coating monitoring (ICM) sensors are two types of sensors which have been reported for efficient damage detection in real engineering fields. For...
Aiming at the ordering problem of the key spare parts whose life is consistent with Weibull distribution, an optimization method based on residual life prediction is proposed considering both residual life and the lead time. Firstly, the Weibull distribution parameters of the component life are estimated according to the historical data of the airline and the state change information of the part on...
The space-based precipitation products are commonly used for regional and/or global hydrologic modelling and climate studies. However, the accuracy of onboard satellite measurements is limited due to the spatial-temporal sampling limitations, especially for extreme events such as very heavy or light rain. On the other hand, ground-based radar is more mature science for quantitative precipitation estimation...
The last decades have witnessed the development of degradation modeling based remaining useful life (RUL) estimation, especially for Wiener process based degradation models. However, most researchers paid their attention to the drift coefficient representing the degradation rate, while much fewer eyes focus on the diffusion coefficient. The under consideration of diffusion process may cause bias in...
Underwater target tracking, which aims to achieve accurate position of underwater target, has been one of the key issues in the underwater research. However, due to the unique underwater environment such as extraneous interference and weak communication, how to realize the accurate tracking of underwater target becomes a challenging problem. In this paper, we are concerned with the problem of underwater...
In this paper, we consider the nonlinear filtering by using information geometric approach. Under the principle of Bayesian, the filtering problem has been converted to Bayesian estimation. Based on the estimation conditional on the measurement, the posterior probability density functions (PDFs) have constructed a statistical manifold. With the information geometric approach, the nonlinear characteristic...
Soft sensor has been widely used for estimating product quality or other important process variables when online analyzers are not available. In order to cope with estimation performance deterioration when process variables abruptly change, a new soft sensor modeling method based on auxiliary error neuro-fuzzy model is proposed. The model mean square error (MSE) is used as an evaluating index in traditional...
In this paper, an improved estimation of distribution algorithm (EDA) is proposed and applied to the identification of ARMA model parameters. The system parameter identification problem is transformed into the optimization problem in high dimensional parameter space. Based on the traditional EDA algorithm, the parameters of preliminary estimation and data selection are added to improve the speed of...
In this paper, we describe a set of robust algorithms for group-wise registration using both rigid and non-rigid transformations of multiple unlabelled point-sets with no bias toward a given set. These methods mitigate the need to establish a correspondence among the point-sets by representing them as probability density functions where the registration is treated as a multiple distribution alignment...
In condition monitoring systems, condition indicators(CIs) are usually used to describe health state of equipment. Usually, detailed fault isolation is conducted by various diagnoses model, threshold alarming is used to detect exceptions. However, how to set threshold that performs best become a problem. Based on ROC analysis, a condition indicator threshold optimization method is proposed in this...
A new digital modulation technique, named Sigma Shift Keying (SSK), is introduced with minimal transmitter power output (TPO) and very low probability of intercept (POI). Standard deviations (or variances, respectively) of zero mean Gaussian noise signals are being digitally modulated instead of shift keying of sinusoidal carrier signals. Following a theoretical analysis of achievable bit error rate...
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