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91. Finally, the software was made, in the VC program environment, about the emulation mode of combine the situation assessment and threat estimate with the Bayesian net.

92. Fifthly, A risk analysis implementation flow based on Bayesian Networks(BNs) for risk analysis module is presented. In addition, the BNs is applied to risk analysis module of a real case.

93. With the development of the Bayesian networks on knowledge representation and reasoning algorithms,(www.Sentencedict.com) the methods for SA based on the BNs technology become a hot topic in the domain of SA.

94. The principle of Recursive Bayesian estimation was introduced which was the basis of Particle filter, and the significance of importance function to the design of particle filter was illustrated.

95. We adopt a way of attribute selection based on word entropy, use vectors which are represented by word frequency, and deduce its corresponding Bayesian formula.

96. The purpose of this paper is to summary the literatures on tests of portfolio mean-variance efficiency in the framework of classical statistics and Bayesian inference.

97. The results show that the Bayesian method is capable of handling both statistical uncertainty and fatigue fun-outs.

98. Then we will introduce some common-used filtering techniques. Particle filter which is based on Bayesian estimation and Monte Carlo method will be emphasized.

99. Two different global localization methods based on Bayesian estimation theory are investigated in the paper.

100. A risk evaluation model in software project investment based on Bayesian Networks(BNs) is presented in this paper.

101. BFS ( Bayesian Forecasting System ) is one of theoretic frames to produce probabilistic hydrological forecasts.

102. The applicability of third-part test process is illustrated as Bayesian Nash Equilibrium of the compound strategies game model, and a discussion on cooperation possibility is made with Shapley value.

103. It is mainly of two kinds, Naive Bayesian Classification and Bayesian Belief Network Classification.

104. We review the basic principle, formalism, and philosophy of Bayesian inference and discuss its application in the context of the analytic continuation problem.

105. In random environment, a measuring model based on Bayesian estimation is given. In this model, prior distribution is obtained through conjugate law.

106. Then the image noises are removed using Bayesian estimation, producing the preliminary denoised image after reconstruction.

107. This paper builds a Bayesian inference network model based on the Rough Sets and Reason Rules and apply it to fulfill the medical data mining work.

108. Methods of GM(1,1) gray prediction, Bayesian and antecedence index were employed.

109. To solve localization problems of autonomous robots, self-localization methods based on Bayesian filter theory are investigated.

110. Bayesian network is a powerful knowledge representation for decomposing joint probability ( or probability density ).

111. Two different global localization methods based on Bayesian estimation theory are mainly investigated in the paper.

112. Chapter 2 gives an overview on Bayesian decision theory firstly. To overcome the weakness of MLE, we bring discriminative training methods for hidden Markov models into speech evaluation system.

113. Finally, calculations of simulations are performed, which show that the expected Bayesian estimation method is feasible and easy to operate.

114. A probability model based on Bayesian principles is given to measure the semantic association from a concept to its direct-related concept in domain ontology.

115. In this paper, the Bayesian sequential estimation of the parameter about acceptance test of products is studied.

116. Bayesian reliability sequential testing method is gave out for exponential distribution.

117. Bayesian feedback cloud model is constructed with the combination of human being's apriority and feature of cloud model.

118. If Bayesian inference used to describe the problem, the posterior probability density function of earth model describes the solution of a geophysical inverse problem.

119. Based on finite mixture models, we apply Bayesian method to compositional data and ordinal data clustering.

120. The algorithm handles uncertain information with Bayesian inference, giving a quantitative evaluation of the security state of a system and eliminating false alarms effectively.