Using the plot of growing single-parameter Markov processes on a single-parameter Markov process, we constructed successfully a class of important two-parameter processes which are called MM-class processes and whose ...Using the plot of growing single-parameter Markov processes on a single-parameter Markov process, we constructed successfully a class of important two-parameter processes which are called MM-class processes and whose two parameters are unequal in status. We have researched if MM-class processes possess the various two-parameter Markov properties. The definitions of the latter can be found in refs. [1]—[3]. For the definition of the展开更多
通过Jeffreys无信息先验分布描述了Gamma退化过程中参数的相关性,由贝叶斯模型得到各参数满条件分布,使用马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法得到参数后验期望估计,最后给出可靠度评价模型。工程实例表明,所得可靠...通过Jeffreys无信息先验分布描述了Gamma退化过程中参数的相关性,由贝叶斯模型得到各参数满条件分布,使用马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法得到参数后验期望估计,最后给出可靠度评价模型。工程实例表明,所得可靠性评估较独立情形更为保守,能够更早地给出产品修理建议。同时,仿真表明,可靠度要求越高,相关与独立情形寿命估计结果偏差越大,0.9999可靠度下偏差率最大可达9.26%。展开更多
Let X=(X) be a two-parameter *-Markov process with a transition function (p1, p2, p), where X, takes values in the state space (Er,), T=[0,)2. For each r T, let f, be a measurable transformation of (E,) into the state...Let X=(X) be a two-parameter *-Markov process with a transition function (p1, p2, p), where X, takes values in the state space (Er,), T=[0,)2. For each r T, let f, be a measurable transformation of (E,) into the state space (E’r, ). Set Y,=f,(X,), r T. A sufficient condition is given for the process Y=(Yr) still to be a two-parameter *-Markov process with a transition function in terms of transition function (p1, p2, p) and fr. For *-Markov families of two-parameter processes with a transition function, a similar problem is also discussed.展开更多
I. INTRODUCTION AND DEFINITIONS In this report, we shall give a simple counterexample to negative Theorem 1 and Proposition 3 (c)(ii)in [1] and explain the difference between the large-past Markov property and *-Marko...I. INTRODUCTION AND DEFINITIONS In this report, we shall give a simple counterexample to negative Theorem 1 and Proposition 3 (c)(ii)in [1] and explain the difference between the large-past Markov property and *-Markov property. Thereby some mistakes are cleared up.展开更多
Based on confusions between hidden Markov model (HMM) states, a state-restructuring method was proposed. In the method, HMM states were restructured by sharing Gaussian components with their related states, and the re...Based on confusions between hidden Markov model (HMM) states, a state-restructuring method was proposed. In the method, HMM states were restructured by sharing Gaussian components with their related states, and the re-estimation to the increased-parameters, i.e., the inter-state weights, was derived under the expectation maximization (EM) framework. Experiments were performed on speaker-independent, large vocabulary, continuous Mandarin speech recognition. Experimental results showed that the state-restructured systems outperformed the baseline, and achieve significant improvement on recognition accuracy compared with the conventional parameter-increasing method. Such comparative results confirmed that the state-restructuring method was efficient.展开更多
基金Project supported by the National Natural Science Foundation of China
文摘Using the plot of growing single-parameter Markov processes on a single-parameter Markov process, we constructed successfully a class of important two-parameter processes which are called MM-class processes and whose two parameters are unequal in status. We have researched if MM-class processes possess the various two-parameter Markov properties. The definitions of the latter can be found in refs. [1]—[3]. For the definition of the
文摘通过Jeffreys无信息先验分布描述了Gamma退化过程中参数的相关性,由贝叶斯模型得到各参数满条件分布,使用马尔科夫链蒙特卡洛(Markov Chain Monte Carlo,MCMC)方法得到参数后验期望估计,最后给出可靠度评价模型。工程实例表明,所得可靠性评估较独立情形更为保守,能够更早地给出产品修理建议。同时,仿真表明,可靠度要求越高,相关与独立情形寿命估计结果偏差越大,0.9999可靠度下偏差率最大可达9.26%。
基金Project supported by the National Natural Science Foundation of Chinathe Guangdong Provincial Natural Science Foundation of China the Foundation of Zhongshan University Advanced Research Center.
文摘Let X=(X) be a two-parameter *-Markov process with a transition function (p1, p2, p), where X, takes values in the state space (Er,), T=[0,)2. For each r T, let f, be a measurable transformation of (E,) into the state space (E’r, ). Set Y,=f,(X,), r T. A sufficient condition is given for the process Y=(Yr) still to be a two-parameter *-Markov process with a transition function in terms of transition function (p1, p2, p) and fr. For *-Markov families of two-parameter processes with a transition function, a similar problem is also discussed.
文摘I. INTRODUCTION AND DEFINITIONS In this report, we shall give a simple counterexample to negative Theorem 1 and Proposition 3 (c)(ii)in [1] and explain the difference between the large-past Markov property and *-Markov property. Thereby some mistakes are cleared up.
文摘Based on confusions between hidden Markov model (HMM) states, a state-restructuring method was proposed. In the method, HMM states were restructured by sharing Gaussian components with their related states, and the re-estimation to the increased-parameters, i.e., the inter-state weights, was derived under the expectation maximization (EM) framework. Experiments were performed on speaker-independent, large vocabulary, continuous Mandarin speech recognition. Experimental results showed that the state-restructured systems outperformed the baseline, and achieve significant improvement on recognition accuracy compared with the conventional parameter-increasing method. Such comparative results confirmed that the state-restructuring method was efficient.
