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    Directed acyclic graph machine learning system
    11.
    发明授权
    Directed acyclic graph machine learning system 有权

    公开(公告)号：US11443198B1

    公开(公告)日：2022-09-13

    申请号：US17522062

    申请日：2021-11-09

    Applicant: SAS Institute Inc.

    Inventor： Xilong Chen ,  Tao Huang ,  Jan Chvosta

    IPC: G06N5/02 ,  G06F17/16

    Abstract: A computing device learns a directed acyclic graph (DAG). An SSCP matrix is computed from variable values defined for observation vectors. A topological order vector is initialized that defines a topological order for the variables. A loss value is computed using the topological order vector and the SSCP matrix. (A) A neighbor determination method is selected. (B) A next topological order vector is determined relative to the initialized topological order vector using the neighbor determination method. (C) A loss value is computed using the next topological order vector and the SSCP matrix. (D) (B) and (C) are repeated until each topological order vector is determined in (B) based on the neighbor determination method. A best topological vector is determined from each next topological order vector based on having a minimum value for the computed loss value. An adjacency matrix is computed using the best topological vector and the SSCP matrix.

    Techniques for estimating compound probability distribution by simulating large empirical samples with scalable parallel and distributed processing
    12.
    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 producing statistically correct and efficient combinations of multiple simulated posterior samples
    13.
    发明授权
    Techniques for producing statistically correct and efficient combinations of multiple simulated posterior samples 有权

    公开(公告)号：US10095660B2

    公开(公告)日：2018-10-09

    申请号：US14210361

    申请日：2014-03-13

    Applicant: SAS Institute Inc.

    Inventor： Christian Macaro ,  Jan Chvosta ,  Mark Roland Little

    IPC: G06N7/02 ,  G06N7/06 ,  G06F17/18 ,  G06N7/00

    Abstract: Various embodiments are generally directed to techniques for producing statistically correct and efficient combinations of multiple simulated posterior samples from MCMC and related Bayesian sampling schemes are described. One or more chains from a Bayesian posterior distribution of values may be generated. It may be determine whether the one or more chains have reached stationarity through parallel processing on a plurality of processing nodes. Based upon the determination, each of the one or more chains that have reached stationarity through parallel processing on the plurality of processing nodes may be sorted. The one or more sorted chains may be resampled through parallel processing on the plurality of processing nodes. The one or more resampled chains may be combined. Other embodiments are described and claimed.

    Compact representation of multivariate posterior probability distribution from simulated samples
    14.
    发明授权
    Compact representation of multivariate posterior probability distribution from simulated samples 有权

    公开(公告)号：US09672193B2

    公开(公告)日：2017-06-06

    申请号：US14217707

    申请日：2014-03-18

    Applicant: SAS Institute Inc.

    Inventor： Christian Macaro ,  Jan Chvosta ,  Mark Roland Little

    IPC: G06F9/44 ,  G06N7/02 ,  G06N7/06 ,  G06F17/18 ,  G06N7/00

    CPC classification number: G06F17/18 ,  G06N7/005

    Abstract: Various embodiments are directed to techniques for selecting a subset of a set of simulated samples. A computer-program product including instructions to cause a computing device to order a plurality of UPDFs by UPDF value, wherein the plurality of UPDFs is associated with a chain of draws of a set of simulated samples, wherein each draw comprises multiple parameters and the UPDF values map to parameter values of the parameters; select a subset of the plurality of UPDFs based on the subset of the plurality of UPDFs having UPDF values within a range corresponding to a range of parameter values to include in a subset of the set of simulated samples; and transmit an indication of a draw comprising parameters having parameter values to include in the subset of the set of simulated samples, wherein the indication identifies the draw by associated UPDF. Other embodiments are described and claimed.

    Causal inference and policy optimization system based on deep learning models
    16.
    发明授权
    Causal inference and policy optimization system based on deep learning models 有权

    公开(公告)号：US11354566B1

    公开(公告)日：2022-06-07

    申请号：US17507376

    申请日：2021-10-21

    Applicant: SAS Institute Inc.

    Inventor： Xilong Chen ,  Douglas Allan Cairns ,  Jan Chvosta ,  David Bruce Elsheimer ,  Yang Zhao ,  Ming-Chun Chang ,  Gunce Eryuruk Walton ,  Michael Thomas Lamm

    IPC: G06N3/04 ,  G16H50/50 ,  G06N5/04 ,  G06N3/00

    Abstract: A treatment model that is a first neural network is trained to optimize a treatment loss function based on a treatment variable t using a plurality of observation vectors by regressing t on x(1),z. The trained treatment model is executed to compute an estimated treatment variable value {circumflex over (t)}i for each observation vector. An outcome model that is a second neural network is trained to optimize an outcome loss function by regressing y on x(2) and an estimated treatment variable t. The trained outcome model is executed to compute an estimated first unknown function value {circumflex over (α)}(xi(2)) and an estimated second unknown function value {circumflex over (β)}(xi(2)) for each observation vector. An influence function value is computed for a parameter of interest using {circumflex over (α)}(xi(2)) and {circumflex over (β)}(xi(2)). A value is computed for the predefined parameter of interest using the computed influence function value.

    Automatic spatial regression system
    17.
    发明授权
    Automatic spatial regression system 有权

    公开(公告)号：US11328225B1

    公开(公告)日：2022-05-10

    申请号：US17524406

    申请日：2021-11-11

    Applicant: SAS Institute Inc.

    Inventor： Guohui Wu ,  Jan Chvosta ,  Wan Xu ,  Gunce Eryuruk Walton ,  Xilong Chen

    IPC: G06N20/00 ,  G06F17/16

    Abstract: A computing device selects a trained spatial regression model. A spatial weights matrix defined for observation vectors is selected, where each element of the spatial weights matrix indicates an amount of influence between respective pairs of observation vectors. Each observation vector is spatially referenced. A spatial regression model is selected from spatial regression models, initialized, and trained using the observation vectors and the spatial weights matrix to fit a response variable using regressor variables. Each observation vector includes a response value for the response variable and a regressor value for each regressor variable of the regressor variables. A fit criterion value is computed for the spatial regression model and the spatial regression model selection, initialization, and training are repeated until each spatial regression model is selected. A best spatial regression model is selected and output as the spatial regression model having an extremum value of the fit criterion value.

    TECHNIQUES FOR ESTIMATING COMPOUND PROBABILITY DISTRIBUTION BY SIMULATING LARGE EMPIRICAL SAMPLES WITH SCALABLE PARALLEL AND DISTRIBUTED PROCESSING
    18.
    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
    20.
    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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