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61. Decision theory, statistical classification, maximum likelihood and Bayesian estimation, non - parametric methods, unsupervised learning and clustering.

62. It proposed the improvement Bayesian sorting algorithm by the foundation of the empirical data.

63. Kalman-Filter-based optimal observation scheme is a Bayesian method,(Sentence dictionary) which improves observation locations by minimizing the expectation of the root mean square deviation(RMSD) of the analysis field.

64. Then parameters of the cost - sensitivity Bayesian networks are evaluated based on cost - sensitivity loss function.

65. To improve the accuracy and speed in cycle-accurate power estimation, this paper uses multiple dimensional coefficients to build a Bayesian inference dynamic power model.

66. The type and parameters of the prior data distribution should be known in Bayesian method.

67. The paper presents a kind of bayesian inference of simple signal in dynamic measurement. The simple measured signals and output signals are thought of as realizations of stochastic process.

68. By using a data set comprising mitochondrial genomes from 177 humans, we estimate substitution rates for various data partitions by using Bayesian phylogenetic analysis with a relaxed molecular clock.

69. In addition, the multiple fuzzy hypothesis testing is studied from the Bayesian statistical viewpoint.

70. The Bayesian classification technique which is relatively more mature in field of fuzzy recognition is applied to the detection technique of P2P deep flow inspection.

71. Objective To investigate the accuracy of Bayesian fitting for predicting aminoglycoside concentrations using Abbottbase pharmacokinetic systems program (PKS).

72. Under the law of maximum shoddy probability, it will have more practical significance for the probabilistic analysis of degree of compaction to use decision analysis and Bayesian method.

73. In Bayesian reference, marginal likelihood function involve to compute high dimensional complex integrand. So exactly to compute marginal likelihood is often difficult.

74. Upon this frame, use matrix Lie group to express the rotation space, then base on Bayesian estimation framework, calculate the minimum mean squared error bounds which use the matrix Lie groups.

75. Based on wavelet domain Hidden Markov model, a novel speckle suppression method for medical ultrasound images is presented which combines Bayesian estimation and homomorphic filtering.

76. The algorithms used in maneuvering target tracking can be classified as methods based on maximum likelihood estimation and methods based on Bayesian estimation.

77. This paper describes a methodology based on ILP for upgrading na ? ve Bayesian classifiers to first-order logic.

78. In this paper, the conditional mean and its applications to Bayesian estimation of the parameters and reliability measures of weibull distribition and power-law process are discussed.

79. Monte Carlo method is used to sample geophysical model according to Bayesian inference.

80. Modeling with Bayesian belief network has been a powerful tool to solve many uncertainty problems.

81. Through the analysis of the composite base price and the introduction to the Bayesian decision, the Bayes theorem is led into determining the quoted price with the composite base price.

82. Bayesian Belief Network(BBN)is a graphic model that encodes joint probability distribution among uncertain variables, it express a potential dependent relationship between variables.

83. Confidence intervals play a similar role in frequentist statistics to the credibility interval in Bayesian statistics.

84. The method is to derive the maximum a posteriori estimate of the regions and the boundaries by using Bayesian inference and neighborhood constraints based on Markov random fields(MRFs) models.

85. It is seen that hierarchical Bayesian estimation is more efficient than maximum likelihood estimation through Monte Carlo simulation and an example.

86. Then we employ Bayesian classifier to classify these questions. Answer extraction is the most crucial part for question answering system.

87. Bayesian neural network, each error value and the right to be treated as random variables, and their apriori probability distribution is the normal distribution.

88. The most common statistical approach is called bayesian inference and is explained in detail in another IBM developerWorks article (see Resources).

89. Then the optimal estimation of the object state parameters is obtained by Bayesian inference.

90. Geller et al. 122 proposed that the question of precursor test can be addressed using a Bayesian approach where each failed attempt at prediction lowers the apriori probability for the next attempt.