Probabilistic Graphical Model for Bayesian-Frequentist Anomaly Detection
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Solution Overview
Problem
Existing supply chain machine learning systems fail to leverage Bayesian data analysis algorithms for anomaly detection and are unable to combine frequentist and Bayesian approaches effectively, leading to suboptimal performance in identifying data anomalies.
Innovation Solution
A probabilistic graphical model (PGM) system that integrates both frequentist and Bayesian anomaly detection algorithms, using weighted combinations to identify and correct data anomalies in supply chain networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If frequentist data analysis algorithms are used for anomaly detection, then good performance is achieved over large data sets, but the system fails to leverage prior information effectively
Solution Approach 1:
The patent combines frequentist and Bayesian data analysis algorithms into a unified anomaly detection system. The hybrid approach integrates the strengths of frequentist methods (effective for large datasets) with Bayesian methods (effective at utilizing prior information), allowing the system to leverage prior information while maintaining good performance on large supply chain datasets.
2Loss of information
If Bayesian data analysis algorithms are used for anomaly detection, then prior information is effectively utilized, but the system fails to achieve optimal performance on large data sets
Solution Approach 1:
The patent merges Bayesian algorithms that effectively utilize prior information with frequentist algorithms that perform well on large datasets. This combination allows the system to maintain optimal performance on large supply chain datasets while effectively incorporating prior information through the Bayesian component.
3Ease of manufacture
If only frequentist approaches are used, then the system is simple to implement, but it is unable to combine frequentist and Bayesian approaches to gain benefits of both
Solution Approach 1:
The patent implements a hybrid system that merges frequentist and Bayesian approaches, enabling the system to gain the benefits of both methodologies while maintaining practical implementability through a unified framework for anomaly detection in supply chain networks.
Data Source
AI summary
A system and method are disclosed for applying machine learning to identify anomalous supply chain data that generates a probabilistic graphical model based on training data from historical attributes of a supply chain comprising supply chain entities to represent the performance of the supply chain entities in the supply chain, standardizes input features data related to the probabilistic graphical model, performs data anomaly detection within the probabilistic graphical model using one or more frequentist data anomaly detection algorithms, performs data anomaly detection within the probabilistic graphical model using one or more Bayesian data anomaly detection algorithms, combines according to one or more weighting methods, the data anomaly detection performed using one or more frequentist data anomaly detection algorithms with the data anomaly detection performed using one or more Bayesian data anomaly detection algorithms, and detects in response to the combining, an anomaly within the standardized input features data.