Toeplitz covariance matrix estimation has many uses in statistical signal processing due to the stationarity assumption of many signals. For some applications, further constraints may exist on the maximum lag at which...
详细信息
ISBN:
(纸本)1424407281
Toeplitz covariance matrix estimation has many uses in statistical signal processing due to the stationarity assumption of many signals. For some applications, further constraints may exist on the maximum lag at which the correlation function is non-zero and thereby giving rise to a band-Toeplitz covariance matrix. In this paper, an existing em-algorithm for Toeplitz estimation is generalized to the case of band-Toeplitz estimation. In addition, the Cramer-Rao lower-bound for unbiased band-Toeplitz covariance matrix estimation is derived and through simulations it is shown that the proposed estimator achieves the bound for medium and large sample-sizes.
For model based analysis of computer and telecommunication systems an appropriate representation of arrival and service processes is very important. Especially representations that can be used in analytical or numeric...
详细信息
ISBN:
(纸本)3540408142
For model based analysis of computer and telecommunication systems an appropriate representation of arrival and service processes is very important. Especially representations that can be used in analytical or numerical solution approaches like phase type (PH) distributions or Markovian arrival processes (MAPS) are useful. This paper presents an algorithm to fit the parameters of a MAP according to measured data. The proposed algorithm is of the expectation-maximization (em-) type and extends known approaches for the parameter fitting of PH-distributions and hidden Markov chains. It is shown that the algorithm generates MAPS which approximate traces very well and especially capture the autocorrelation in the trace. Furthermore the approach can be combined with other more efficient but less accurate fitting techniques by computing initial MAPS with those techniques and improving these MAPS with the approach presented in this paper.
Leaf area index (LAI) is a biophysical variable that is related to atmosphere-biosphere exchange of CO2. One way to obtain LAI value is by the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products...
详细信息
Leaf area index (LAI) is a biophysical variable that is related to atmosphere-biosphere exchange of CO2. One way to obtain LAI value is by the Moderate Resolution Imaging Spectroradiometer (MODIS) biophysical products (LAI MODIS). The LAI MODIS has been used to improve the physiological principles predicting growth (3-PG) model within a Bayesian Network (BN) set-up. The MODIS time series, however, contains gaps caused by persistent clouds, cloud contamination, and other retrieval problems. We therefore formulated the em-algorithm to estimate the missing MODIS LAI values. The em-algorithm is applied to three different cases: successive and not successive two winter seasons, and not successive missing MODIS LAI during the time study of 26 successive months at which the performance of the BN is assessed. Results show that the MODIS LAI is estimated such that the maximum value of the mean absolute error between the original MODIS LAI and the estimated MODIS LAI by em-algorithm is 0.16. This is a low value, and shows the success of our approach. Moreover, the BN output improves when the em-algorithm is carried out to estimate the inconsecutive missing MODIS LAI such that the root mean square error reduces from 1.57 to 1.49. We conclude that the em-algorithm within a BN can handle the missing MODIS LAI values and that it improves estimation of the LAI.
The theory of multilevel hierarchical data Expectation Maximization (em)-algorithm is introduced via discrete time Markov chain (DTMC) epidemic models. A general model for a multilevel hierarchical discrete data is de...
详细信息
The theory of multilevel hierarchical data Expectation Maximization (em)-algorithm is introduced via discrete time Markov chain (DTMC) epidemic models. A general model for a multilevel hierarchical discrete data is derived. The observed sample.. in the system is a stochastic incomplete data, and the missing data.. exhibits a multilevel hierarchical data structure. The em-algorithm to find ML-estimates for parameters in the stochastic system is derived. Applications of the em-algorithm are exhibited in the two DTMC models, to find ML-estimates of the system parameters. Numerical results are given for influenza epidemics in the state of Georgia (GA), USA.
The expectation maximization (em) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabiliti...
详细信息
The expectation maximization (em) algorithm has received considerable attention in the area of positron emitted tomography (PET) as a restoration and reconstruction technique. In this paper, the restoration capabilities of the emalgorithm when applied to radiographic images is investigated. This application does not involve reconstruction. The performance of the emalgorithm is quantitatively evaluated using a "perceived" signal-to-noise ratio (SNR) as the image quality metric. This perceived SNR is based on statistical decision theory and includes both the observer's visual response function and a noise component internal to the eye-brain system. For a variety of processing parameters, the relative SNR (ratio of the processed SNR to the original SNR) is calculated and used as a metric to compare quantitatively the effects of the emalgorithm with two other image enhancement techniques: global contrast enhancement (windowing) and unsharp mask filtering. The results suggest that the emalgorithm's performance is superior when compared to unsharp mask filtering and global contrast enhancement for radiographic images which contain objects smaller than 4 mm.
An em-algorithm is proposed to study a mixture model for the analysis of count data. Iterative procedures for estimating the parameters are given for various discrete distributions including Binomial, Negative Binomia...
详细信息
An em-algorithm is proposed to study a mixture model for the analysis of count data. Iterative procedures for estimating the parameters are given for various discrete distributions including Binomial, Negative Binomial, and Poisson. The em procedure is applied to a set of PVC data in Berry (1987).
Due to the increase in volatile power generation facilities, the need for flexible modeling options of an energy network is growing. One approach consists of a cellular architecture whose hierarchy levels are less pro...
详细信息
Due to the increase in volatile power generation facilities, the need for flexible modeling options of an energy network is growing. One approach consists of a cellular architecture whose hierarchy levels are less pronounced. Such an architecture is provided by the Loop Circle Arc theory (LoCA theory). Each cell consists of essentially uniform basic building blocks, such as a storage unit, an energy converter, and a source and load, as well as an interface to the next cell. Based on this theory, a model of N households connected to a Circle is created. In order to report the demand of the connected households to the next cell, the Arc, via the interface, it is necessary to know the summed power values. Since the households generally represent stochastic processes, the densities associated with the households are estimated under the assumption of measured consumption values over a 24-hour period. Using the em-algorithm, mixed distribution densities are estimated based on normal distribution densities for each household and superimposed accordingly. In this way, in addition to the expected total power consumption, a variance can be given at the same time. This allows not only an estimation of the energy to be made available at certain times. It is also possible to simplify the network, since the N households can be approximated by the time evolution of the expected overall power consumption values. (C) 2021 Published by Elsevier Ltd.
This paper gives a model of customer choice behavior modeling based on a combination of decision-making processes by applying latent class model based on emalgorithm. This model can apply for the choice problems of m...
详细信息
ISBN:
(纸本)9789898111111
This paper gives a model of customer choice behavior modeling based on a combination of decision-making processes by applying latent class model based on emalgorithm. This model can apply for the choice problems of multi services and multi brands under various decision-making processes. In addition to the model based on emalgorithm, we tried some conventional models and compared them. The model based on emalgorithm enables us to know what kinds of customers are classified into a certain class. Moreover, we could construct more accuracy model than conventional model and found the existence of two decision-making processes.
A given group of protein sequences of different lengths is considered as resulting from random transformations of independent random ancestor sequences of the same preset smaller length, each produced in accordance wi...
详细信息
ISBN:
(纸本)9783642160004
A given group of protein sequences of different lengths is considered as resulting from random transformations of independent random ancestor sequences of the same preset smaller length, each produced in accordance with an unknown common probabilistic profile. We describe the process of transformation by a Hidden Markov Model (HMM) which is a direct generalization of the PAM model for amino acids. We formulate the problem of finding the maximum likelihood probabilistic ancestor profile and demonstrate its practicality. The proposed method of solving this problem allows for obtaining simultaneously the ancestor profile and the posterior distribution of its HMM, which permits efficient determination of the most probable multiple alignment of all the sequences. Results obtained on the BAliBASE 3.0 protein alignment benchmark indicate that the proposed method is generally more accurate than popular methods of multiple alignment such as CLUSTALW, DIALIGN and ProbAlign.
The using of Gabor function and texture features for pattern recognition in satellite images, principal component analysis (pca) and em-algorithm are discussed in this paper. Based on the presented in this article res...
详细信息
ISBN:
(纸本)9781509067428
The using of Gabor function and texture features for pattern recognition in satellite images, principal component analysis (pca) and em-algorithm are discussed in this paper. Based on the presented in this article results of the experiment, the algorithm based on em-algorithm and Gabor wavelets demonstrated high quality of the result and low time characteristics. The novelty of the method under consideration is the use of the Gabor wavelet to isolate texture features in conjunction with the use of the em-algorithm for image segmentation and the pca algorithm to reduce the dimensionality of the feature space of the extracted images.
暂无评论