Projector Inferencing for Bayesian Model Selection Accuracy
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Solution Overview
Problem
Existing data analysis systems lack adequate user-provided data intake and processing capabilities, leading to inaccurate iterative analysis of complex phenomena.
Innovation Solution
An apparatus and method using projector inferencing to compare and select between a first and second model by projecting data through a first and second projector, incorporating a decision theoretic Bayesian approach to decouple model estimation from decision making, and employing metamodels like machine-learning algorithms to enhance data projection and selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple projectors are run to improve model selection accuracy, then the reliability of data analysis is improved, but the device complexity and computational resources required increase
Solution Approach 1:
The system segments the model selection process by running multiple specialized projectors (first projector, second projector, etc.), each designed to project specific types of data or analyze particular aspects of the phenomenon. This segmentation allows each projector to be optimized for its specific function while collectively providing comprehensive and accurate model selection through their coordinated results.
2Measurement precision
If projector inferencing with variance of noise is incorporated to reduce uncertainty, then the measurement precision is improved, but the computational processing time increases
Solution Approach 1:
The patent replaces traditional mechanical or deterministic computational approaches with a probabilistic inferencing system that incorporates variance of noise measurements. Instead of using deterministic projection methods, the system employs statistical inferencing that naturally accounts for uncertainty, thereby achieving more precise measurement of projection outcomes without requiring exhaustive computational enumeration of all possible variations.
3Adaptability or versatility
If a decision theoretic Bayesian approach is used to decouple model estimation from decision making, then the adaptability of the system is improved, but the device complexity increases
Solution Approach 1:
The system segments the analytical process into distinct phases: model estimation phase and decision-making phase. The Bayesian approach enables the first phase to operate independently, generating probabilistic estimates without being constrained by specific decision criteria. This segmentation allows each phase to be optimized separately and adapted to different requirements, thereby improving overall system adaptability despite the increased complexity of the Bayesian processing involved.
Data Source
AI summary
An apparatus for model selection between a first model and a second model using projector inferencing is provided. The apparatus includes a processor and a memory connected to the processor. The memory contains instructions configuring the processor to receive an entity datum from an entity device and a second datum from a client device connected to the processor. The second datum describes matching the entity datum based on a preferred allocation with target values using the models. The processor may run two projectors capable of outputting operational values by projecting the entity datum over a defined duration. The processor may score operational values to target values using a fuzzy inferencing system. Scoring the operational values may include classifying an operational value and the second datum to categories organized sequentially in multiple discrete increments.