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公开(公告)号：US20230185883A1
公开(公告)日：2023-06-15
申请号：US17862510
申请日：2022-07-12
Applicant: SAS Institute Inc.
Inventor： Christian Macaro , Fedor Reva , Rocco Claudio Cannizzaro
CPC classification number: G06K9/6272 , G06K9/6223 , G06K9/6269 , G06Q10/0635
Abstract: A computing device determines a disaggregated solution vector of a plurality of variables. A first value is computed for a known variable using a predefined density distribution function, and a second value is computed for an unknown variable using the computed first value, a predefined correlation value, and a predefined aggregate value. The predefined correlation value indicates a correlation between the known variable and the unknown variable. A predefined number of solution vectors is computed by repeating the first value and the second value computations. A solution vector is the computed first value and the computed second value. A centroid vector is computed from solution vectors computed by repeating the computations. A predefined number of closest solution vectors to the computed centroid vector are determined from the solution vectors. The determined closest solution vectors are output.
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公开(公告)号：US11704388B2
公开(公告)日：2023-07-18
申请号：US17862510
申请日：2022-07-12
Applicant: SAS Institute Inc.
Inventor： Christian Macaro , Fedor Reva , Rocco Claudio Cannizzaro
IPC: G06F17/11 , G06F18/2413 , G06Q10/0635 , G06F18/2411 , G06F18/23213
CPC classification number: G06F18/24137 , G06F17/11 , G06F18/23213 , G06F18/2411 , G06Q10/0635
Abstract: A computing device determines a disaggregated solution vector of a plurality of variables. A first value is computed for a known variable using a predefined density distribution function, and a second value is computed for an unknown variable using the computed first value, a predefined correlation value, and a predefined aggregate value. The predefined correlation value indicates a correlation between the known variable and the unknown variable. A predefined number of solution vectors is computed by repeating the first value and the second value computations. A solution vector is the computed first value and the computed second value. A centroid vector is computed from solution vectors computed by repeating the computations. A predefined number of closest solution vectors to the computed centroid vector are determined from the solution vectors. The determined closest solution vectors are output.
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公开(公告)号：US11016871B1
公开(公告)日：2021-05-25
申请号：US17129536
申请日：2020-12-21
Applicant: SAS Institute Inc.
Inventor： Rocco Claudio Cannizzaro , Christian Macaro
Abstract: Resource consumption associated with executing a bootstrapping process on a computing device can be reduced. For example, a system can receive a dataset including observations. The system can then instantiate one or more thread objects configured to execute a bootstrapping process that involves multiple iterations. Each iteration can involve: determining a respective set of probabilities based on an observation distribution associated with the dataset, executing a function based on the respective set of probabilities to determine a respective metric value, and storing the respective metric value in memory. This iterative process may be faster and less computationally intensive than traditional bootstrapping approaches. After completing the iterative process, the system may access the memory to obtain the metric values, determine a distribution of metric values based on at least some of the metric values, and store the distribution of metric values in the memory for further use.
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Patent Agency Ranking
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
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.
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5.发明授权公开(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
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.
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公开(公告)号：US09710428B2
公开(公告)日：2017-07-18
申请号：US14210301
申请日：2014-03-13
Applicant: SAS Institute Inc.
Inventor： Christian Macaro , Jan Chvosta , Mark Roland Little
Abstract: Techniques for automated Bayesian posterior sampling using Markov Chain Monte Carlo and related schemes are described. In an embodiment, one or more values in an accuracy phase for a system configured for Bayesian sampling may be initialized. Sampling may be performed in the accuracy phase based upon the one or more values to generate a plurality of samples. The plurality of samples may be evaluated based upon one or more accuracy criteria. The accuracy phase may be exited when the plurality of samples meets the one or more accuracy criteria. Other embodiments are described and claimed.
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公开(公告)号：US11010451B2
公开(公告)日：2021-05-18
申请号：US14210259
申请日：2014-03-13
Applicant: SAS Institute Inc.
Inventor： Christian Macaro , Jan Chvosta , Mark Roland Little
Abstract: Techniques for automated Bayesian posterior sampling using Markov Chain Monte Carlo and related schemes are described. In an embodiment, one or more values in a stationarity phase for a system configured for Bayesian sampling may be initialized. Sampling may be performed in the stationarity phase based upon the one or more values to generate a plurality of samples. The plurality of samples may be evaluated based upon one or more stationarity criteria. The stationarity phase may be exited when the plurality of samples meets the one or more stationarity criteria. Other embodiments are described and claimed.
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公开(公告)号：US10146741B2
公开(公告)日：2018-12-04
申请号：US14217858
申请日：2014-03-18
Applicant: SAS Institute Inc.
Inventor： Christian Macaro , Jan Chvosta , Mark Roland Little
Abstract: Various embodiments are directed to techniques for deriving a sample representation from a random sample. A computer-program product includes instructions to cause a first computing device to fit an empirical distribution function to a marginal probability distribution of a variable within a first sample portion of a random sample to derive a partial marginal probability distribution approximation, wherein the random sample is divided into multiple sample portions distributed among multiple computing devices; fit a first portion of a copula function to a multivariate probability distribution of the first sample portion, wherein the copula function is divided into multiple portions; and transmit an indication of a first likelihood contribution of the first sample portion to a coordinating device to cause a second computing device to fit a second portion of the copula function to a multivariate probability distribution of a second sample portion. Other embodiments are described and claimed.
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