公开(公告)号：US20240346289A1

公开(公告)日：2024-10-17

申请号：US18530798

申请日：2023-12-06

Applicant: SAS Institute Inc.

Inventor： Sylvie Tchumtchoua Kabisa ,  Xilong Chen ,  Gunce Eryuruk Walton ,  David Bruce Elsheimer ,  Ming-Chun Chang

IPC: G06N3/045 ,  G06N3/084

CPC classification number: G06N3/045 ,  G06N3/084

Abstract: A point estimate value for an individual is computed using a Bayesian neural network model (BNN) by training a first BNN model that computes a weight mean value, a weight standard deviation value, a bias mean value, and a bias standard deviation value for each neuron of a plurality of neurons using observations. A plurality of BNN models is instantiated using the first BNN model. Instantiating each BNN model of the plurality of BNN models includes computing, for each neuron, a weight value using the weight mean value, the weight standard deviation value, and a weight random draw and a bias value using the bias mean value, the bias standard deviation value, and a bias random draw. Each instantiated BNN model is executed with the observations to compute a statistical parameter value for each observation vector of the observations. The point estimate value is computed from the statistical parameter value.

公开(公告)号：US20240346284A1

公开(公告)日：2024-10-17

申请号：US18529014

申请日：2023-12-05

Applicant: SAS Institute Inc.

Inventor： Sylvie Tchumtchoua Kabisa ,  Xilong Chen ,  Gunce Eryuruk Walton ,  David Bruce Elsheimer ,  Ming-Chun Chang

IPC: G06N3/04 ,  G06N3/08

CPC classification number: G06N3/04 ,  G06N3/08

Abstract: A treatment model trained to compute an estimated treatment variable value for each observation vector of a plurality of observation vectors is executed. Each observation vector includes covariate variable values, a treatment variable value, and an outcome variable value. An outcome model trained to compute an estimated outcome value for each observation vector using the treatment variable value for each observation vector is executed. A standard error value associated with the outcome model is computed using a first variance value computed using the treatment variable value of the plurality of observation vectors, using a second variance value computed using the treatment variable value and the estimated treatment variable value of the plurality of observation vectors, and using a third variance value computed using the estimated outcome value of the plurality of observation vectors. The standard error value is output.

公开(公告)号：US12210954B2

公开(公告)日：2025-01-28

申请号：US18530798

申请日：2023-12-06

Applicant: SAS Institute Inc.

Inventor： Sylvie Tchumtchoua Kabisa ,  Xilong Chen ,  Gunce Eryuruk Walton ,  David Bruce Elsheimer ,  Ming-Chun Chang

IPC: G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084

Abstract: A point estimate value for an individual is computed using a Bayesian neural network model (BNN) by training a first BNN model that computes a weight mean value, a weight standard deviation value, a bias mean value, and a bias standard deviation value for each neuron of a plurality of neurons using observations. A plurality of BNN models is instantiated using the first BNN model. Instantiating each BNN model of the plurality of BNN models includes computing, for each neuron, a weight value using the weight mean value, the weight standard deviation value, and a weight random draw and a bias value using the bias mean value, the bias standard deviation value, and a bias random draw. Each instantiated BNN model is executed with the observations to compute a statistical parameter value for each observation vector of the observations. The point estimate value is computed from the statistical parameter value.

公开(公告)号：US12165031B2

公开(公告)日：2024-12-10

申请号：US18529014

申请日：2023-12-05

Applicant: SAS Institute Inc.

Inventor： Sylvie Tchumtchoua Kabisa ,  Xilong Chen ,  Gunce Eryuruk Walton ,  David Bruce Elsheimer ,  Ming-Chun Chang

IPC: G06N3/04 ,  G06N3/045 ,  G06N3/08 ,  G06N3/084

Abstract: A treatment model trained to compute an estimated treatment variable value for each observation vector of a plurality of observation vectors is executed. Each observation vector includes covariate variable values, a treatment variable value, and an outcome variable value. An outcome model trained to compute an estimated outcome value for each observation vector using the treatment variable value for each observation vector is executed. A standard error value associated with the outcome model is computed using a first variance value computed using the treatment variable value of the plurality of observation vectors, using a second variance value computed using the treatment variable value and the estimated treatment variable value of the plurality of observation vectors, and using a third variance value computed using the estimated outcome value of the plurality of observation vectors. The standard error value is output.

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.