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1. 发明申请

US20090144034A1 TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY 有权
标题翻译：用于自动发现的时间调制生成概率模型
公开(公告)号：US20090144034A1
公开(公告)日：2009-06-04
申请号：US11949061
申请日：2007-12-03
申请人： Aleksandr Simma , Moises Goldszmidt
发明人： Aleksandr Simma , Moises Goldszmidt
IPC分类号： G06F17/10 , G06G7/62
CPC分类号： H04L41/0663 , H04L41/142 , H04L41/145
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.
摘要翻译：客户端或服务器中的不同信道或不同业务之间的依赖关系可以从对构成这些信道或业务的分组的进入和传出的时间的观察来确定。概率模型可用于正式表征这些依赖性。概率模型可以用于列出输入分组和各种信道或服务的输出分组之间的依赖性，并且可以用于建立围绕这些信道或服务的不同事件之间的因果关系的预期强度。概率模型的参数可以基于现有知识，或者可以使用基于关于感兴趣事件的时间的观察的统计技术来拟合。可以观察事件之间的预期发生时间，并且依赖性可以根据概率模型来确定。

2. 发明授权

US07895146B2 Time modulated generative probabilistic models for automated causal discovery that monitors times of packets 有权
标题翻译：用于自动病因发现的时间调制生成概率模型，用于监视数据包的时间
公开(公告)号：US07895146B2
公开(公告)日：2011-02-22
申请号：US11949061
申请日：2007-12-03
申请人： Aleksandr Simma , Moises Goldszmidt
发明人： Aleksandr Simma , Moises Goldszmidt
IPC分类号： G06F17/00
CPC分类号： H04L41/0663 , H04L41/142 , H04L41/145
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.
摘要翻译：客户端或服务器中的不同信道或不同业务之间的依赖关系可以从对构成这些信道或业务的分组的进入和传出的时间的观察来确定。概率模型可用于正式表征这些依赖性。概率模型可以用于列出输入分组和各种信道或服务的输出分组之间的依赖性，并且可以用于建立围绕这些信道或服务的不同事件之间的因果关系的预期强度。概率模型的参数可以基于现有知识，或者可以使用基于关于感兴趣事件的时间的观察的统计技术来拟合。可以观察事件之间的预期发生时间，并且依赖性可以根据概率模型来确定。

3. 发明申请

US20110209001A1 TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY 审中-公开
标题翻译：用于自动发现的时间调制生成概率模型
公开(公告)号：US20110209001A1
公开(公告)日：2011-08-25
申请号：US13100412
申请日：2011-05-04
申请人： Aleksandr Simma , Moises Goldszmidt
发明人： Aleksandr Simma , Moises Goldszmidt
IPC分类号： G06F11/07 , G06F15/16 , G06N5/02
CPC分类号： H04L41/0663 , H04L41/142 , H04L41/145
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.
摘要翻译：客户端或服务器中的不同信道或不同业务之间的依赖关系可以从对构成这些信道或业务的分组的进入和传出的时间的观察来确定。概率模型可用于正式表征这些依赖性。概率模型可以用于列出输入分组和各种信道或服务的输出分组之间的依赖性，并且可以用于建立围绕这些信道或服务的不同事件之间的因果关系的预期强度。概率模型的参数可以基于现有知识，或者可以使用基于关于感兴趣事件的时间的观察的统计技术来拟合。可以观察事件之间的预期发生时间，并且依赖性可以根据概率模型来确定。

4. 发明授权

US07958069B2 Time modulated generative probabilistic models for automated causal discovery using a continuous time noisy-or (CT-NOR) models 有权
标题翻译：使用连续时间噪声或（CT-NOR）模型进行自动因果发现的时间调制生成概率模型
公开(公告)号：US07958069B2
公开(公告)日：2011-06-07
申请号：US13007643
申请日：2011-01-16
申请人： Aleksandr Simma , Moises Goldszmidt
发明人： Aleksandr Simma , Moises Goldszmidt
IPC分类号： G06E1/00
CPC分类号： H04L41/0663 , H04L41/142 , H04L41/145
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.
摘要翻译：客户端或服务器中的不同信道或不同业务之间的依赖关系可以从对构成这些信道或业务的分组的进入和传出的时间的观察来确定。概率模型可用于正式表征这些依赖性。概率模型可以用于列出输入分组和各种信道或服务的输出分组之间的依赖性，并且可以用于建立围绕这些信道或服务的不同事件之间的因果关系的预期强度。概率模型的参数可以基于现有知识，或者可以使用基于关于感兴趣事件的时间的观察的统计技术来拟合。可以观察事件之间的预期发生时间，并且依赖性可以根据概率模型来确定。

5. 发明申请

US20110113004A1 TIME MODULATED GENERATIVE PROBABILISTIC MODELS FOR AUTOMATED CAUSAL DISCOVERY USING A CONTINUOUS TIME NOISY-OR (CT-NOR) MODELS 有权
标题翻译：使用连续时间噪声或（CT-NOR）模型的自动发现的时间调制生成概率模型
公开(公告)号：US20110113004A1
公开(公告)日：2011-05-12
申请号：US13007643
申请日：2011-01-16
申请人： Aleksandr Simma , Moises Goldszmidt
发明人： Aleksandr Simma , Moises Goldszmidt
IPC分类号： G06N5/02
CPC分类号： H04L41/0663 , H04L41/142 , H04L41/145
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.
摘要翻译：客户端或服务器中的不同信道或不同业务之间的依赖关系可以从对构成这些信道或业务的分组的进入和传出的时间的观察来确定。概率模型可用于正式表征这些依赖性。概率模型可以用于列出输入分组和各种信道或服务的输出分组之间的依赖性，并且可以用于建立围绕这些信道或服务的不同事件之间的因果关系的预期强度。概率模型的参数可以基于现有知识，或者可以使用基于关于感兴趣事件的时间的观察的统计技术来拟合。可以观察事件之间的预期发生时间，并且依赖性可以根据概率模型来确定。

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