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Time modulated generative probabilistic models for automated causal discovery that monitors times of packets
Time modulated generative probabilistic models for automated causal discovery that monitors times of packets
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机译：用于自动因果发现的时间调制生成概率模型，可监控数据包的时间
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摘要
Dependencies between different channels or different services in a client or server may be determined from the observation of the times of the incoming and outgoing of the packets constituting those channels or services. A probabilistic model may be used to formally characterize these dependencies. The probabilistic model may be used to list the dependencies between input packets and output packets of various channels or services, and may be used to establish the expected strength of the causal relationship between the different events surrounding those channels or services. Parameters of the probabilistic model may be either based on prior knowledge, or may be fit using statistical techniques based on observations about the times of the events of interest. Expected times of occurrence between events may be observed, and dependencies may be determined in accordance with the probabilistic model.
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