| Patent application number | Description | Published |
|---|
| 20090016470 | TARGETED MAXIMUM LIKELIHOOD ESTIMATION - A method for obtaining an estimator for a distribution pertaining to a dataset is provided. In an illustrative embodiment, the method includes obtaining a dataset; determining a question pertaining to the data; determining an initial estimator descriptive of a distribution of the data; and selectively modifying the initial estimator based on the question, yielding a targeted estimator in response thereto. In a more specific embodiment, selectively modifying the initial estimator includes applying an additional equation and/or a fluctuation function to the initial estimator to yield the targeted estimator, wherein the additional equation or fluctuation function parameter (ε) causes the initial estimator to fluctuate or change as a function of the parameter. The fluctuation function is chosen so that when the parameter ε is set to zero, the initial estimator is not fluctuated. The targeted estimator and a fluctuation function may be employed in an accompanying targeted Bayesian method that involves mapping a prior distribution of a target feature to a posterior distribution of the target feature. | 01-15-2009 |
| 20090210371 | DATA ADAPTIVE PREDICTION FUNCTION BASED ON CANDIDATE PREDICTION FUNCTIONS - In one embodiment, a method for predicting an outcome is provided. The method comprises: determining a known data set of data, the known data set of data including an input variable and an output variable; determining a plurality of candidate prediction functions, each prediction function adapted to determine a candidate predicted outcome for the output variable using a different algorithm; determining a combination of the plurality of candidate prediction functions based on the known data set; determining a second set of data, the second set of data including data for the input variable; and determining, based on the input variable, a predicted outcome for the output variable using a data adaptive prediction function, wherein the data adaptive prediction function uses the combination of candidate predicted outcomes from the plurality of candidate prediction functions determined using the data from the input variable to determine the predicted outcome. | 08-20-2009 |
| 20110047106 | CONSTUCTION OF TARGETED ADAPTIVE DESIGNS AND MAXIMUM LIKELIHOOD LEARNING FOR ADAPTIVE DESIGNS - In one embodiment, a method for targeted adaptive design processing is provided. The method comprises: determining data for a first stage of an adaptive design, each stage of the adaptive design being a set of experiments that are adapted based on a design mechanism, the data for a stage including data for the set of experiments for that stage; determining an estimator based on the data for the first stage; and analyzing the data using the estimator to adapt the design mechanism for a next stage of the adaptive design, the adaptive design mechanisms sign mechanism being considered more optimal to yield data for estimating a target parameter; and outputting the design mechanism for use in a second stage of the experiment. The method further comprises determining a second estimator for the adaptive design usable to estimate the target parameter of the adaptive design based on the analysis. | 02-24-2011 |
