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A novel approach to unsupervised stochastic model-based image segmentation is presented and the problems of
parameter estimation and image segmentation are formulated as Bayesian learning. In order to draw samples corresponding
to di erent c
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用监督参数估计中的贝叶斯方法估计条件概率密度的参数u-Parameter estimation of conditional probability density using Bayesian methods in supervised parameter estimation u
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虚拟力的无线传感网络覆盖,利用贝叶斯原理估计混合logit模型的参数,单径或多径瑞利衰落信道仿真。- Virtual power wireless sensor network coverage, Bayesian parameter estimation principle mixed logit model, Single path or multipath Rayleigh fading channel simulation.
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GPS and INS navigation program, Bayesian parameter estimation principle mixed logit model, High simulation efficiency.
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