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排序方式: 共有1578条查询结果,搜索用时 31 毫秒
1.
关新民 《新疆大学学报(理工版)》2002,19(1):95-99
介绍了采用MATLAB V5.2提供的模糊逻辑工具箱来设计研究电孤纺电极调节系统中的模糊--PD控制器,讨论了在SIMULINK环境下模糊-PD控制器的参数自调速原理、结构、建立模糊控制规则库和模糊推论方法,并给出仿真结果与结论。 相似文献
2.
3.
Thomas Marlow 《International Journal of Theoretical Physics》2006,45(7):1247-1257
We attempt a justification of a generalisation of the consistent histories programme using a notion of probability that is valid for all complete sets of history propositions. This consists of introducing Cox's axioms of probability theory and showing that our candidate notion of probability obeys them. We also give a generalisation of Bayes' theorem and comment upon how Bayesianism should be useful for the quantum gravity/cosmology programmes.
PACS: 02.50.Cw;03.65.Ta;04.60.-m. 相似文献
4.
We present a Bayesian theory of object identification. Here, identifying an object means selecting a particular observation from a group of observations (variants), this observation (the regular variant) being characterized by a distributional model. In this sense, object identification means assigning a given model to one of several observations. Often, it is the statistical model of the regular variant, only, that is known. We study an estimator which relies essentially on this model and not on the characteristics of the “irregular” variants. In particular, we investigate under what conditions this variant selector is optimal. It turns out that there is a close relationship with exchangeability and Markovian reversibility. We finally apply our theory to the case of irregular variants generated from the regular variant by a Gaussian linear model. 相似文献
5.
James M. Calvin 《Journal of Global Optimization》1993,3(2):223-232
A sequential Bayesian method for finding the maximum of a function based on myopically minimizing the expected dispersion of conditional probabilities is described. It is shown by example that an algorithm that generates a dense set of observations need not converge to the correct answer for some priors on continuous functions on the unit interval. For the Brownian motion prior the myopic algorithm is consistent; for any continuous function, the conditional probabilities converge weakly to a point mass at the true maximum. 相似文献
6.
本文通过模拟研究,讨论了最大似然方法和Bayes方法在分析结构方程模型中的相似点和不同之处。 相似文献
7.
Guido Consonni Piero Veronese Eduardo Gutirrez-Pea 《Journal of multivariate analysis》2004,88(2):335-364
Reference analysis is one of the most successful general methods to derive noninformative prior distributions. In practice, however, reference priors are often difficult to obtain. Recently developed theory for conditionally reducible natural exponential families identifies an attractive reparameterization which allows one, among other things, to construct an enriched conjugate prior. In this paper, under the assumption that the variance function is simple quadratic, the order-invariant group reference prior for the above parameter is found. Furthermore, group reference priors for the mean- and natural parameter of the families are obtained. A brief discussion of the frequentist coverage properties is also presented. The theory is illustrated for the multinomial and negative-multinomial family. Posterior computations are especially straightforward due to the fact that the resulting reference distributions belong to the corresponding enriched conjugate family. A substantive application of the theory relates to the construction of reference priors for the Bayesian analysis of two-way contingency tables with respect to two alternative parameterizations. 相似文献
8.
Keith Jonathan M. Kroese Dirk P. Bryant Darryn 《Methodology and Computing in Applied Probability》2004,6(1):29-53
A recent development of the Markov chain Monte Carlo (MCMC) technique is the emergence of MCMC samplers that allow transitions between different models. Such samplers make possible a range of computational tasks involving models, including model selection, model evaluation, model averaging and hypothesis testing. An example of this type of sampler is the reversible jump MCMC sampler, which is a generalization of the Metropolis–Hastings algorithm. Here, we present a new MCMC sampler of this type. The new sampler is a generalization of the Gibbs sampler, but somewhat surprisingly, it also turns out to encompass as particular cases all of the well-known MCMC samplers, including those of Metropolis, Barker, and Hastings. Moreover, the new sampler generalizes the reversible jump MCMC. It therefore appears to be a very general framework for MCMC sampling. This paper describes the new sampler and illustrates its use in three applications in Computational Biology, specifically determination of consensus sequences, phylogenetic inference and delineation of isochores via multiple change-point analysis. 相似文献
9.
无失效数据的Bayes和多层Bayes分析 总被引:3,自引:0,他引:3
本文推广了文献[6]的结果,对指数分布无失效数据的失效率,给出了Bayes估计、Bayes置信上限以及多层Bayes估计,从而可以得到无失效数据可靠度的估计,最后,结合实际问题进行了计算。 相似文献
10.
Bruni C. Bruni R. De Santis A. Iacoviello D. Koch G. 《Journal of Optimization Theory and Applications》2002,115(1):67-96
In this paper, a procedure is presented which allows the optimal reconstruction of images from blurred noisy data. The procedure relies on a general Bayesian approach, which makes proper use of all the available information. Special attention is devoted to the informative content of the edges; thus, a preprocessing phase is included, with the aim of estimating the jump sizes in the gray level. The optimization phase follows; existence and uniqueness of the solution is secured. The procedure is tested against simple simulated data and real data. 相似文献