Dyadic Bayesian Model Structuring for Probabilistic Programming
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Solution Overview
Problem
Current probabilistic programming is low-level, irregular, and unstructured, lacking a structure for defining parameters, predicting outputs, and observing data, which hinders code re-use and efficient implementation of standard tasks like training and prediction.
Innovation Solution
A dyadic Bayesian model is introduced, comprising a pair of probabilistic functions for prior and sampling distributions, along with a sampler and learner object, to provide a structured approach for modeling data generating processes and performing tasks such as training and prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If probabilistic programming is implemented in a low-level, irregular, and unstructured manner, then flexibility in writing variations of models is improved, but code re-use and efficiency for standard tasks deteriorate
Solution Approach 1:
The patent transforms the unstructured probabilistic programming approach into a structured one by defining specific parameter categories (parameters, hyperparameters, data) and their relationships. This structural parameterization enables code re-use while maintaining flexibility, as models can be varied by changing parameters within the established structure rather than rewriting entire model definitions.
Solution Approach 2:
The patent creates a universal structured framework that can handle multiple probabilistic programming tasks (training, prediction, model selection, mixture models) through a common architecture. The standardized structure with defined parameters, hyperparameters, and data relationships serves as a multi-functional foundation that improves code re-use across different probabilistic programming scenarios.
2Ease of operation
If probabilistic programming lacks a structured format for defining parameters and predicting outputs, then ease of writing custom models is improved, but implementation efficiency for standard tasks deteriorates
Solution Approach 1:
The patent introduces a structured parameter system that categorizes model components (parameters, hyperparameters, data) with defined relationships. This structure makes model writing easier by providing a clear template, while simultaneously improving implementation efficiency through standardized operations that can be automatically generated and optimized.
Solution Approach 2:
The patent establishes a preliminary structured framework that pre-defines the relationships between parameters, hyperparameters, and data. This preliminary structuring enables automatic generation of inference algorithms and standard task implementations, improving efficiency without restricting the ease of writing custom models within the framework.
3Device complexity
If probabilistic programming uses an unstructured approach, then simplicity of model definition is improved, but ability to perform model selection and mixture models deteriorates
Solution Approach 1:
The patent maintains simplicity by using a clear parameter-based structure rather than complex procedural code. The structured format with defined parameters, hyperparameters, and relationships actually simplifies model definition while enabling advanced capabilities like model selection and mixture models through standardized operations on these parameters.
Solution Approach 2:
The structured framework provides universal operations that work across different model types and complexities. The same structural elements (parameters, hyperparameters, data relationships) support both simple model definitions and complex tasks like model selection and mixture models, eliminating the trade-off between simplicity and versatility.
Data Source
AI summary
There is provided a method and system for modeling a data generating process. The method includes generating a dyadic Bayesian model including a pair of probabilistic functions representing a prior distribution and a sampling distribution, and modeling a data generating process based on the dyadic Bayesian model using observed data. The method includes generating a learner object for the dyadic Bayesian model. The method further includes training the dyadic Bayesian model with the learner object based on the observed data to produce a trained dyadic Bayesian model. The method also includes generating a posterior distribution over parameters based on the trained dyadic Bayesian model. The method also further includes generating a posterior predictive distribution based on the posterior distribution. The method also includes predicting an outcome of observable variables based on the posterior predictive distribution.