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In order to avoid failures or diminish the impact of them, it is important to deal with them before its occurrence. Some existing approaches for online failure prediction are insufficient to handle the upcoming failures beforehand, because they cannot predict the failures early enough to execute workaround operations for failure. To solve this problem, we have developed a method to estimate the prediction...
Speckle hinders information in synthetic aperture radar (SAR) images and makes automatic information extraction very difficult. The Bayesian approach allows us to perform the despeckling of an image while preserving its texture and structures. This model-based approach relies on a prior model of the scene. This paper presents an evaluation of two despeckling and texture extraction model-based methods...
In recent years, one mode of data dissemination has become extremely popular, which is the deep web. A key characteristics of deep web data sources is that data can only be accessed through the limited query interface they support. This paper develops a methodology for mining the deep web. Because these data sources cannot be accessed directly, thus, data mining must be performed based on sampling...
Extracting and analyzing the interesting patterns from spatio-temporal databases, have drawn a great interest in various fields of research. Recently, a number of experiments have explored the problem of spatial or temporal data mining, and some clustering algorithms have been proposed. However, not many studies have been dealing with the integration of spatial data mining and temporal data mining...
It is well known that the key of Bayesian classifier learning is to balance the two important issues, that is, the exploration of attribute dependencies in high orders for ensuring a sufficient flexibility in approximating the ground-truth dependencies, and the exploration of low orders for ensuring a stable probability estimate from limited training samples. By allowing one-order attribute dependencies,...
Learning Bayesian networks can be examined as the combination of parameter learning and structure learning. Parameter learning is estimation of the conditional probabilities (dependencies) in the network. Structural learning is the estimation of the topology (links) of the network. The structure of the network can be known or unknown, and the variables can be expressed as complete and incomplete data...
Target intention inference is an important aspect of situation assessment. The evidence system of targets' intention inference is discussed according to the independent relationship between targets' intention and input evidence. The targets' intention probability inference model is proposed based on static Bayesian network. In order to expand the application domain and predigest the parameter learning...
In this paper, we propose a robust approach to access point (AP) selection problem for the indoor location tracking. It takes the environments changes into account and makes use of residuals ranking algorithm to select those APs least sensitive to the environment changes in indoor location tracking, we call it ResidualRanking method, also we make an improvement of residual computing according to the...
We consider the problem of parameter estimation of Markovian models where the exact computation of the partition function is not possible or computationally too expensive with MCMC methods. The main idea is then to approximate the expression of the likelihood by a simpler one where we can either have an analytical expression or compute it more efficiently. We consider two approaches: Variational Bayes...
A framework for a new type of estimation of distribution algorithms (EDAs) is developed. It is similar to the Bayesian optimization algorithm (BOA) except that it replaces Bayesian network model with estimation of schema distribution based on maximum entropy. As structure learning of Bayesian network is not needed, it reduces the computational cost. The experimental results show that the new algorithms...
The paper deals with estimation of a state with discrete values. The proposed estimation technique is evolved as an application of Bayesian filtering to a state-space model with discrete distribution. The example of filtering is shown with Bernoulli distributions. The considered problem is one of the items aiming at filtering with mixed continuous and discrete state. Illustrative experiments demonstrate...
In this paper, new classes of lower bounds on the outage error probability and on the minimum mean-square-error (MSE) in Bayesian parameter estimation are proposed. The outage error probability and the MSE are important criteria in parameter estimation. However, computation of these terms is usually not tractable. The proposed outage error probability class of lower bounds is derived using reverse...
Approximate text search is a basic technique to handle recognized text that contains recognition errors. This paper proposes an approximate string search for recognized texturing a statistical similarity model focusing on parameter estimation. The main contribution of this paper is to propose a parameter estimation algorithm using variational Bayesian expectation maximization technique. We applied...
The primary motivation of using Bayesian statistics in reliability analysis is the ability to incorporate prior knowledge with limited testing results in a formal procedure. This idea is particularly suitable for high reliable systems, which cannot afford enough samples to meet the confidence requirement in reliability demonstration test. The problem of appropriate choice of prior distribution is...
Series system reliability is based on the minimum life time of its components. Its dual, the parallel system, is based on maximum. Here, we consider the statistical analysis of both, series and parallel, systems where the components follow the Weibull parametric model. Our perspective is Bayesian. Due to the mathematical complexity, to obtain the posterior distribution we use the Metropolis-Hasting...
Bayesian method has been widely used for parameters evaluation and decision making. However, existing studies paid more attention on the construction of prior distribution and method engineering applications. The previous researches had fewer discourses on the reliability influence analysis of prior information or prior distribution. In this paper, based on the coherence analysis between prior information...
This paper uses the hierarchical Bayes way to estimate the product reliability when prior density function of R is in form of pi(R/alpha) square Ralpha and 0 < alpha < k. Under the condition of the binomial distribution with zero-failure data, the value of the product reliability is estimated. The conclusion generalizes the relevant conclusion of papers[6][8][9][10][12]. The conclusion has certain...
The consistency test between prior information and field information using parametric method is investigated. A method to compute fitting goodness is presented. The case when there are multi-source prior information is discussed. Based on the fitting goodness, a new method is provided to determine the weighting factor of each prior distribution. Corresponding analysis is performed, when the product...
At present, the methods of enterprise financial risk warning emphasize static function dependency or dynamic propagation of time series, which results in a unconsistent combination of the static and dynamic information. In this paper, a dynamic hierarchical naive Bayesian network model is developed for enterprise financial risk warning. The process of using the model and the methods of analyzing contribution...
This paper presents model based despeckling and soil moisture estimation using TerraSAR-X data. The impact of despeckling on soil moisture estimation is presented and compared with real-ground measurements. This paper presents the model based despeckling using a maximum a posteriori approach. The prior is modeled using the auto-binomial model and Gauss Markov random field (GMRF). Both models belong...
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