1. Model description and likelihood function with grouped data.

2. Then, the likelihood function of probability casual model is taken as antigen of immune genetic algorithm and solution of fault diagnosis as antibodies.

3. Then, based on the Bayesian theorem, sample likelihood function and priori distribution of the model, the posteriori distribution of parameters was derived.

4. A united logarithmic likelihood function based on the statistic probability model of image stochastic filed was built by using the multi-frame images.

5. In the paper, the fuzzy likelihood function is utilized to cluster sample data.

6. Using hierarchical hierarchical likelihood function and the process of estimating model are derived.

7. In addition, our likelihood function is also relevant to several psychological findings.

8. A new fuzzy likelihood function is used to denotes the similarity degree of two fuzzy sets.

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

10. It can be computed from knowledge of the likelihood function defining the system.

11. With maximizing the marginal likelihood function of hyper-parameters, the optimal weights are acquired, i. e. the reconstructed image.

12. New concepts of the likelihood function and the fuzzy alternation entropy are given based on fuzzy information theory, and their properties are discussed.

12. try its best to gather and create good sentences.

13. In this part, we give the description of the competing risk mixture model and the likelihood function with grouped data. 3. Asymptotic properties of MLE.

14. Using hierarchical likelihood approach, the multidimensional integral is avoided, and the hierarchical likelihood function and the process of estimating model ar.

15. This paper describes a new nonlinear filtering algorithm (NLF) for tracking maneuvering targets, presents reasonable maneuvering likelihood function, derives estimating equations.

16. In traditional multiple hypothesis tracking(MHT) algorithm, only target location information has been used for truth likelihood function of tracks.

17. The ARMA model was used to describe the prior distribution of observed discharge and the AR model was adopted to simulate the likelihood function of forecasting error.

18. The posterior probability is computed from the prior and the likelihood function via Bayes' theorem.

19. However, as the MLE requires obtaining the parameters of the high order non-linear likelihood function when it reaches the global maximum, this makes the MLE approach has very huge calculation burden.

20. Experimental results show that evolutionary algorithm can reach the maximization of the global likelihood function.

21. Based on this idea, we can know the logarithm likelihood function of these approximately independent random variables. Then we can evaluate parameters by the idea of MLE.

22. But it was difficult to calculate, because censored data in the form of samples of the likelihood function is very complex.