公开(公告)号：US11501041B1

公开(公告)日：2022-11-15

申请号：US17730476

申请日：2022-04-27

Applicant: SAS Institute Inc.

Inventor： Xilong Chen ,  Yang Zhao ,  Sylvie T. Kabisa ,  David Bruce Elsheimer

IPC: G06F30/23 ,  G06F111/10 ,  G06F30/27

Abstract: One example described herein involves a system receiving task data and distribution criteria for a state space model from a client device. The task data can indicate a type of sequential Monte Carlo (SMC) task to be implemented. The distribution criteria can include an initial distribution, a transition distribution, and a measurement distribution for the state space model. The system can generate a set of program functions based on the task data and the distribution criteria. The system can then execute an SMC module to generate a distribution and a corresponding summary, where the SMC module is configured to call the set of program functions during execution of an SMC process and apply the results returned from the set of program functions in one or more subsequent steps of the SMC process. The system can then transmit an electronic communication to the client device indicating the distribution and its corresponding summary.

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.

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.