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RP094_VOL.5-2234
- artithe intelligent virtual environment in an artificial fish of virtual auditory system is designed in this paper. Firstly, the model of artificial fish of auditory system in intelligent virtual environment (IVE) is built. Secondly, two-laye
xm_LeakMax_MainProg_Alarm
- BN网络建模，Alarm标准案例中的节点参数学习算法，基于Noisy-Max模型-Based Noisy-Max model，Parameters Learning of Bayesian Networks for Alarm Case
xm_LeakMax_MainProg_Hepar
- 基于因果独立模型的Hepar网络参数学习算法，Hepar网络是BN学习中常用的标准案例-Parameters Learning of Bayesian Networks Based on Independence of Causal Influence Model for Hepar Case
xm_CPTDistanceCompare
- BN网络参数之间的KL距离 (Kullback–Leibler Distance) 计算，用于比较相似度-BN KL distance between network parameters calculation, used to compare similarity
vgg16
- 在使用深度神经网络时我们一般推荐使用大牛的组推出的和成功的网络。如最近的google团队推出的BN-inception网络和inception-v3以及微软最新的深度残差网络ResNET。(In the use of deep neural network we generally recommend the use of cattle group launched and successful network. Such as the recent google team launched B
BayesKit
- 贝叶斯网络，又称信念网络(Belief Network, BN), 或有向无环图模型，是由一个有向无环图(DAG,Directed acyclic graphical model)和条件概率分布(即知道P(xi|parent(xi))发生的概率构成，其中parent(xi)为指向xi的直接父节点)。它是一种模拟人类推理过程中因果关系的不确定性处理模型，其网络拓朴结构是一个有向无环图(DAG)。(Bayesian networks, also known as belief networks (B
BN例子
- 贝叶斯网络应用的一个例子，吸烟患病模型 1建立贝叶斯网络结构并制定条件概率表 2画出建立好的贝叶斯网络 3输入证据,进行推理 4显示推理结果(An example of a Bayesian network application, the smoking model 1 Establish a Bayesian network structure and establish a conditional probability table 2 draw a well-establis