This paper applies a new expectation maximization (em) based identification method to estimate a generic FitzHugh-Nagumo (FHN) model under unknown Gaussian measurement noise. It is well noted that such FHN model is an...
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ISBN:
(纸本)9781728158556
This paper applies a new expectation maximization (em) based identification method to estimate a generic FitzHugh-Nagumo (FHN) model under unknown Gaussian measurement noise. It is well noted that such FHN model is an elementary neuronal dynamics description and plays a significant role in deep understanding and basic analysis for complicated mechanisms of some neural diseases. Different from the most existing relevant identification methods, the applied new method is additionally capable of supplying users with variance estimation for the unknown measurement noise corrupting on the membrane potential apart from model parameter estimates. All unknown parameters can be iteratively estimated by a particle smoothing based em algorithm, which consists of an expectation (E) step and a maximization (M) step. Smoothed joint-state particles are produced by a new particle smoothing algorithm to evaluate an expectation of a log-likelihood function in the E step, and the model parameters and noise variance can then be efficiently optimized by the gradient based methods in the M step. The resulting estimations own global convergence for a relatively wide range of parameter initializations. Finally, good convergence behaviors of the estimated model parameters and noise variance are demonstrated by a numerical simulation for a classic FHN model by using 10 simulation realizations with random initializations varying within +/- 100% of their respective true values.
In this paper, we discuss regression analysis of censored failure time data when there exist missing covariates and more specifically, we will consider interval-censored data, a general form of censored data, and the ...
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In this paper, we discuss regression analysis of censored failure time data when there exist missing covariates and more specifically, we will consider interval-censored data, a general form of censored data, and the nonignorable missing. Although many methods have been proposed in the literature for censored data with missing covariates, they only apply to limited situations and it does not seem to exist an established procedure for the situation discussed here. For the analysis, we employ the semiparametric linear transformation model and develop a two-step estimation procedure. In addition, the asymptotic properties of the resulting estimators are established and a Poisson variable-based em algorithm is provided for the implementation of the proposed estimation procedure. Finally the proposed approach is applied to an Alzheimer Disease study that motivated this investigation. (C) 2020 Elsevier B.V. All rights reserved.
Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estima...
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Vegetation height estimation plays a pivotal role in forest mapping, which significantly promotes the study of environment and climate. This paper develops a general forest structure model for vegetation height estimation using polarimetric interferometric synthetic aperture radar (PolInSAR) data. In simple terms, the temporal decorrelation factor of the random volume over ground model with volumetric temporal decorrelation (RVoG-vtd) is first modeled by random motions of forest scatterers to solve the problem of ambiguity. Then, a novel four-stage algorithm is proposed to improve accuracy in forest height estimation. In particular, to compensate for the temporal decorrelation mainly caused by changes between multiple observations, one procedure of temporal decorrelation adaptive estimation via Expectation-Maximum (em) algorithm is added into the novel method. On the other hand, to extract the features of amplitude and phase more effectively, in the proposed method, we also convert Euclidean distance to a generalized distance for the first time. Assessments of different algorithms are given based on the repeat-pass PolInSAR data of Gabon Lope Park acquired in AfriSAR campaign of German Aerospace Center (DLR). The experimental results show that the proposed method presents a significant improvement of vegetation height estimation accuracy with a root mean square error (RMSE) of 6.23 m and a bias of 1.28 m against LiDAR heights, compared to the results of the three-stage method (RMSE: 8.69 m, bias: 4.81 m) and the previous four-stage method (RMSE: 7.72 m, bias: -2.87 m).
In this paper, we consider a new procedure for estimating parameters in the proportional hazards model with doubly censored data. Computing the maximum likelihood estimator with doubly censored data is often nontrivia...
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In this paper, we consider a new procedure for estimating parameters in the proportional hazards model with doubly censored data. Computing the maximum likelihood estimator with doubly censored data is often nontrivial and requires a certain constraint optimization procedure, which is computationally unstable and sometimes fails to converge. We propose an approximated likelihood and study the maximum approximated likelihood estimator, which is obtained by maximizing the approximated likelihood. In comparison to the maximum likelihood estimator, this new estimator is stable and always converges with an efficient em algorithm we develop. The stability of the new estimator even with moderate sample sizes is amply demonstrated through simulated and real data. For theoretical justification of the approximated likelihood, we show the consistency of the maximum approximated likelihood estimator. (C) 2012 Elsevier B.V. All rights reserved.
The popularity effect in a network, i.e., the phenomenon in which popular nodes become more popular, has been explained through the fitness models considering the node heterogeneity. The "attractive" nodes, ...
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The popularity effect in a network, i.e., the phenomenon in which popular nodes become more popular, has been explained through the fitness models considering the node heterogeneity. The "attractive" nodes, i.e., nodes with large fitness, are likely to have high popularity. The indegree has been regarded as node popularity. When popularity is not given in the form of indegree, however, the interaction between fitness and popularity may not be considered. In this study, we generalize the concept of fitness to capability and propose a capability-popularity dynamic network (CPDN) model. The CPDN model considers the interaction between the popularity and node heterogeneity when popularity is not expressed in indegree. Broad popularity and indegree processes can be covered in the framework of the proposed model. We present the em algorithm combined with a Bayesian inference method to infer the node capability and model parameters. Monte Carlo simulations are performed to show the validity of the CPDN model. We analyze the Twitch following and YouTube subscription networks and examine how the popularity effect works in the network growth with remarkable interpretations. (C) 2020 Published by Elsevier B.V.
Prediction interval (PI) as a method of probabilistic prediction can output the prediction range with a certain degree of confidence. It can give the users more information than point prediction. The noise of data in ...
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ISBN:
(纸本)9781728185262
Prediction interval (PI) as a method of probabilistic prediction can output the prediction range with a certain degree of confidence. It can give the users more information than point prediction. The noise of data in PI is usually assumed as a Gaussian, Laplace or other single distribution. However, these assumptions are not suitable for all the applications. In order to solve this problem, a mixed approach based on Long Short Term Memory Network with bootstrap (LSTM-bootstrapping) and mixed Gaussian distribution (MGD) with Expectation-Maximization (em) algorithm is proposed to forecast intervals for time series. LSTM is chosen here because of its extremely effectiveness for time series prediction. Firstly, LSTM-bootstrapping is employed to calculate the model uncertainties and the point prediction. Afterwards, we assume that the noise satisfies a mixed Gaussian distribution and the em algorithm is applied to estimate the noise uncertainty. Then PI can be acquired by the variances of model and noise uncertainty. The proposed predictive approach is evaluated on wind speed, heteroscedastic wind power and reg capacity price datasets. The results show that our method can solve the uncertainty problem of arbitrary distribution and obtain better performance.
Music is organized by simple physical structures, such as the relationship between the frequencies of tones. We have focused on the frequency ratio between notes and have proposed lattice spaces, which express the rat...
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ISBN:
(纸本)9789897583957
Music is organized by simple physical structures, such as the relationship between the frequencies of tones. We have focused on the frequency ratio between notes and have proposed lattice spaces, which express the ratios of pitches and pulses. Agents produce melodies using distributions in the lattice spaces. In this study, we upgrade the system to analyze existing music. Therefore, the system can obtain the distribution of the pitch in the pitch lattice space and create melodies. We confirm that the system fits the musical features, such as modes and scales of the existing music as GMM. The probability density function in the pitch lattice space is suggested to be suitable for expressing the primitive musical structure of the pitch. However, there are a few challenges of not adapting a 12-equal temperament and dynamic variation of the mode;in this study, we focus on these challenges.
Maritime wireless communication is in the stage of continuous development. Medium and short wave communication has always been the main method of maritime communications. Realizing rapid, efficient, and reliable signa...
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ISBN:
(纸本)9789811365041;9789811365034
Maritime wireless communication is in the stage of continuous development. Medium and short wave communication has always been the main method of maritime communications. Realizing rapid, efficient, and reliable signal transmission is the urgent need for the development of current situation. In this paper, aiming at MF, HF, or VHF radio waves emitted by land, Bayesian linear regression model is used to solve the reflection problem of calm sea surface, and then em (expectation maximization) algorithm is further used to solve the complex Bayesian model to solve the reflection problem of rough sea surface. Furthermore, the advantages of the Bayesian linear regression model over other models, such as the Longley-Rice model, are obtained.
We study properties and parameter estimation of a finite-state, homogeneous, continuous-time, bivariate Markov chain. Only one of the two processes of the bivariate Markov chain is assumed observable. The general form...
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We study properties and parameter estimation of a finite-state, homogeneous, continuous-time, bivariate Markov chain. Only one of the two processes of the bivariate Markov chain is assumed observable. The general form of the bivariate Markov chain studied here makes no assumptions on the structure of the generator of the chain. Consequently, simultaneous jumps of the observable and underlying processes are possible, neither process is necessarily Markov, and the time between jumps of each of the two processes has a phase-type distribution. Examples of bivariate Markov chains include the Markov modulated Poisson process and the batch Markovian arrival process when appropriate modulo counts are used in each case. We develop an expectation-maximization (em) procedure for estimating the generator of a bivariate Markov chain, and we demonstrate its performance. The procedure does not rely on any numerical integration or sampling scheme of the continuous-time bivariate Markov chain. The proposed em algorithm is equally applicable to multivariate Markov chains. (C) 2012 Elsevier B.V. All rights reserved.
In the whole process of data mining, the em algorithm is widely applied to dealing with incomplete data for its numerical stability, simplicity of implementation, reliable global convergence. the main disadvantage of ...
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ISBN:
(纸本)9781538669563
In the whole process of data mining, the em algorithm is widely applied to dealing with incomplete data for its numerical stability, simplicity of implementation, reliable global convergence. the main disadvantage of the em is slow convergence speed, the algorithm is highly dependent on the initial value of the option, In this paper, the clustering results use K-means algorithm as the initial scope of em algorithm, according to the different choice of different characteristics of mining purposes, then use incremental em algorithm (Iem) step by step em iterative refinement repeatedly, it obtains the optimal value of filling missing data quickly and efficiently. it is concluded that the optimal value of filling missing data experimental results show that the algorithm of this paper to speed up the convergence rate, strengthened the stability of clustering, data filling effect is remarkable.
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