Estimation Apparatus Using Feature Data for Model Parameters
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Solution Overview
Problem
Existing methods for estimating models of discrete data before aggregation do not effectively utilize various pieces of information, such as features of areas, times, and spatiotemporal data, leading to suboptimal accuracy in estimation.
Innovation Solution
An estimation apparatus that inputs aggregated data and feature data, determines model parameters using a predetermined function and feature data, and optimizes an objective function to estimate model parameters accurately, incorporating features of areas, times, and spatiotemporal data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional estimation methods are used, then the estimation process is simple, but the accuracy of model estimation is insufficient
Solution Approach 1:
The estimation process is divided into two distinct phases: (1) determining model parameters using feature data through a predetermined function, and (2) estimating discrete data by optimizing the objective function with aggregated data. This segmentation allows each phase to focus on specific tasks, improving overall estimation accuracy while maintaining manageable complexity.
Solution Approach 2:
The model parameters are determined in advance using feature data before the actual estimation of discrete data begins. This preliminary determination of parameters (such as transition probabilities in collective graphical models) provides a solid foundation for the subsequent optimization process, thereby improving estimation accuracy.
2Measurement precision
If various pieces of information (feature data) are incorporated, then the accuracy of estimation is improved, but the complexity of data processing increases
Solution Approach 1:
The predetermined function serves as a universal mechanism that processes various types of feature data (area features, time features, spatiotemporal features) to determine model parameters. This multi-functional approach allows diverse information sources to be integrated systematically, improving estimation accuracy without proportionally increasing processing complexity.
Solution Approach 2:
The predetermined function acts as an intermediary that transforms raw feature data into meaningful model parameters. This intermediate processing step bridges the gap between raw diverse data and the estimation model, enabling effective utilization of various information pieces while managing complexity through structured transformation.
Data Source
AI summary
An estimation apparatus includes: input means for inputting aggregated data in which a plurality of data are aggregated, and feature data representing a feature of the aggregated data; determination means for determining a parameter of a model of the plurality of data before the aggregation of the aggregated data, using a predetermined function and the feature data; and estimation means for estimating a parameter of the function and the plurality of data by optimizing a predetermined objective function, using the aggregated data.