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Subjects: Mathematics >> Statistics and Probability

已有的关于模型平均估计渐近分布理论的研究多是基于局部误设定的假设,[1] 是其中开创性的且最著名的文章之一. 虽然利用局部误设定的假设可以证明模型平均估计渐近分布理论, 但是 [2] 等对此假设提出了不合理性质疑和解释. 本文我们研究[1]中的置信区间估计方法. 证明了在一般参数设定下, 虽然 [1]中的渐近分布理论不一定成立, 但是关于不确定参数的线性函数的置信区间在正态分布误差、线性回归模 型下是有效的, 即置信区间的覆盖率趋于预设定的名义水平. 我们通过模拟研究进一步验证了理论结果. |

Relative Entropy Minimizing-Based Theory of Intelligent Systems

Xi, GuangchengSubjects: Mathematics >> Statistics and Probability

Based on the point of view of neuroethology and cognition-psychology, general frame of theory for intelligent systems is presented by means of principle of relative entropy minimizing in this paper. Cream of the general frame of theory is to present and to prove basic principle of intelligent systems: entropy increases or decreases together with intelligence in the intelligent systems. The basic principle is of momentous theoretical significance and practical significance .From the basic principle can not only derive two kind of learning algorithms (statistical simulating annealing algorithms and annealing algorithms of mean-field theory approximation) for training large kinds of stochastic neural networks,but also can thoroughly dispel misgivings created by second law of thermodynamics on 'peoplespsychology ,hence make one be fully confident of facing life.Because of Human society, natural world, and even universe all are intelligent systems. |

submitted time
2019-07-21
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Entropy-partition of Complex Systems and Emergence of Human Brain’s Consciousness

Xi GuangchengSubjects: Mathematics >> Statistics and Probability

The three frameworks for theories of consciousness taken moust seriously by neuroscientists are that consciousness is a biological state of the brain,the global workspace perspective,and the perspective of higher state. Consciousness is discussed from viewpoint of theory of Entropy—partition of complex system in present article. Human brain’s system self-organizably and adaptively implements partition、aggregation and integration, and consciousness emerges. |

submitted time
2019-01-10
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Gibbsian representation of knowledge in infinite dimensional random neural networks(IDRNN)

xi guangchengSubjects: Mathematics >> Statistics and Probability

Abstract. In studying of a class of random neural network, some of relative researchers have proposed Markov model of neural network. Wherein Markov property of the neural network is based on “assuming”. To reveal mechanism of generating of Markov property in neural network, it is studied how infinite-dimensional random neural network (IDRNN) forms inner Markov representation of environment information in this paper.Because of equivalence between markov property and Gibbsian our conclusion is that knowledge is eventually expressed by extreme Gibbs probability measure—ergodic Gibbs probability measure in IDRNN. This conclusion is also applicable to quantum mechanical level of IDRNN. Hence one can see “ concept “- “ consciousness” is generated at particle(ion) level in the brain and is experienced at the level of the neurons; We have discussed also ergodicity of IDRNN with random neural potential. |

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