Tuple-Based Probability Encoding for Measurement Uncertainty Computation
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Solution Overview
Problem
Modern computing systems lack efficient methods for representing and propagating uncertainty in measurement data, leading to inaccurate decision-making in applications like driverless cars and automated systems, as they typically use single point values that ignore measurement uncertainty.
Innovation Solution
An encoding method for representing and propagating probability distributions associated with measurement uncertainty, allowing computations to efficiently handle uncertainty in data values by generating and manipulating tuples that encode these distributions, ensuring consistent arithmetic operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If single point values are used to represent measurement data, then computation efficiency is improved, but measurement uncertainty information is lost
Solution Approach 1:
The measurement data is segmented into multiple discrete values (e.g., 32 discrete values representing different measurement outcomes) rather than a single point value. Each discrete value represents a possible measurement outcome with its own probability, allowing uncertainty information to be preserved while enabling efficient computation through structured data representation and processing methods.
2Reliability
If probability distributions are fully represented to capture measurement uncertainty, then decision-making accuracy is improved, but computational complexity increases
Solution Approach 1:
The continuous probability distribution is transformed into a discrete representation with a finite number of values (e.g., 32 discrete values). This parameter change from continuous to discrete domain reduces computational complexity while preserving essential uncertainty information needed for accurate decision-making in automated systems.
Solution Approach 2:
The problem is shifted from continuous probability space to discrete probability space by representing measurements as discrete values with associated probabilities. This dimensional transformation enables the use of efficient discrete computation methods while maintaining the ability to represent and propagate measurement uncertainty through the system.
3Quantity of substance
If discrete values are used to represent continuous measurement data, then data storage efficiency is improved, but measurement precision is reduced
Solution Approach 1:
Instead of storing the full continuous measurement data, a discrete copy or representation is created where 32 discrete values approximate the continuous distribution. This copied discrete representation significantly reduces storage requirements while maintaining sufficient precision for decision-making by preserving the probabilistic structure of the measurement uncertainty.
Data Source
AI summary
A computer-implemented method for the encoding of, and computation on, distributions of data, the method comprising: obtaining a first set of data items; obtaining a second set of data items; generating a first tuple containing parameters encoding a probability distribution characterising the distribution of the data items of the first set; generating a second tuple containing parameters encoding a probability distribution characterising the distribution of the data items of the second set in which the parameters used to encode the distribution of the data items of the second set are the same as the parameters used to encode the distribution of the data items of the first set; generating a third tuple using parameters contained within the first tuple and using parameters contained within the second tuple, the third tuple containing parameters encoding a probability distribution representing the result of applying an arithmetic operation on the first probability distribution and the second probability distribution; outputting the third tuple.