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    Techniques for estimating compound probability distribution by simulating large empirical samples with scalable parallel and distributed processing
    1.
    发明授权
    Techniques for estimating compound probability distribution by simulating large empirical samples with scalable parallel and distributed processing 有权

    公开(公告)号：US10325008B2

    公开(公告)日：2019-06-18

    申请号：US15805774

    申请日：2017-11-07

    Applicant: SAS Institute Inc.

    Inventor： Mahesh V. Joshi ,  Richard Potter ,  Jan Chvosta ,  Mark Roland Little

    IPC: G06Q40/08 ,  G06F17/18 ,  G06F17/50

    Abstract: Techniques for estimated compound probability distribution are described herein. Embodiments may include receiving a compound model specification comprising a frequency model and a severity model, the compound model specification including a model error comprising a frequency model error and a severity model error, and determining a number of frequency models and severity models to generate based on the received number of models to generate. Embodiments include generating a plurality of frequency models through perturbation of the frequency model according to the frequency model error, and generating a plurality of severity models through perturbation of the severity model according to the severity model error. Further, embodiments include dividing generation of a plurality of compound model samples among a plurality of distributed worker nodes, and receiving the plurality of compound model samples from the distributed worker nodes, and generating aggregate statistics from the plurality of compound model samples.

    TECHNIQUES FOR ESTIMATING COMPOUND PROBABILITY DISTRIBUTION BY SIMULATING LARGE EMPIRICAL SAMPLES WITH SCALABLE PARALLEL AND DISTRIBUTED PROCESSING
    3.
    发明申请
    TECHNIQUES FOR ESTIMATING COMPOUND PROBABILITY DISTRIBUTION BY SIMULATING LARGE EMPIRICAL SAMPLES WITH SCALABLE PARALLEL AND DISTRIBUTED PROCESSING 审中-公开

    公开(公告)号：US20180060470A1

    公开(公告)日：2018-03-01

    申请号：US15805774

    申请日：2017-11-07

    Applicant: SAS Institute Inc.

    Inventor： Mahesh V. Joshi ,  Richard Potter ,  Jan Chvosta ,  Mark Roland Little

    IPC: G06F17/50 ,  G06F17/18

    CPC classification number: G06F17/18 ,  G06F17/5009 ,  G06F2217/10 ,  G06Q40/08

    Abstract: Techniques for estimated compound probability distribution are described herein. Embodiments may include receiving a compound model specification comprising a frequency model and a severity model, the compound model specification including a model error comprising a frequency model error and a severity model error, and determining a number of frequency models and severity models to generate based on the received number of models to generate. Embodiments include generating a plurality of frequency models through perturbation of the frequency model according to the frequency model error, and generating a plurality of severity models through perturbation of the severity model according to the severity model error. Further, embodiments include dividing generation of a plurality of compound model samples among a plurality of distributed worker nodes, and receiving the plurality of compound model samples from the distributed worker nodes, and generating aggregate statistics from the plurality of compound model samples.

    TECHNIQUES FOR ESTIMATING COMPOUND PROBABILITY DISTRIBUTION BY SIMULATING LARGE EMPIRICAL SAMPLES WITH SCALABLE PARALLEL AND DISTRIBUTED PROCESSING
    4.
    发明申请
    TECHNIQUES FOR ESTIMATING COMPOUND PROBABILITY DISTRIBUTION BY SIMULATING LARGE EMPIRICAL SAMPLES WITH SCALABLE PARALLEL AND DISTRIBUTED PROCESSING 审中-公开
    通过模拟具有可分级并行和分布式处理的大型实验样本估算化合物概率分布的技术

    公开(公告)号：US20160314226A1

    公开(公告)日：2016-10-27

    申请号：US15197691

    申请日：2016-06-29

    Applicant: SAS Institute Inc.

    Inventor： Mahesh V. Joshi ,  Richard Potter ,  Jan Chvosta ,  Mark Roland Little

    IPC: G06F17/50 ,  G06F17/18

    CPC classification number: G06F17/18 ,  G06F17/5009 ,  G06F2217/10 ,  G06Q40/08

    Abstract: Techniques for estimated compound probability distribution are described. An apparatus comprising a configuration component, perturbation component, sample generation controller, an aggregation component, a distribution fitting component, and statistics generation component. The configuration component operative to receive a compound model specification and candidate distribution definition. The perturbation component operative to generate a plurality of models from the compound model specification. The sample generation controller operative to initiate the generation of a plurality of compound model samples from each of the plurality of models. The distribution fitting component to generate parameter values for the candidate distribution definition based on the compound model samples. The statistics generation component to generate approximated aggregate statistics.

    Abstract translation: 描述估计复合概率分布的技术。 一种包括配置组件，扰动组件，样本生成控制器，聚合组件，分布拟合组件和统计生成组件的设备。 配置组件可操作以接收复合模型规范和候选分配定义。 扰动分量可用于从复合模型规范生成多个模型。 样本生成控制器用于从多个模型中的每个模型开始产生多个复合模型样本。 分布拟合组件，用于基于复合模型样本生成候选分布定义的参数值。 生成近似聚合统计信息的统计生成组件。

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