Model Sampling

Introduction

Model sampling, also known as solution sampling or witness enumeration, is the process of generating multiple distinct solutions to a logical formula or constraint system. This technique is crucial in various applications including:

  • Test case generation

  • Statistical analysis

  • Probabilistic inference

  • Verification and validation

Model sampling is a challenging problem due to the following factors: - The complexity of the underlying logical formulas or constraints. - The need for generating a large number of diverse solutions. - Ensuring the uniformity and independence of the samples. - …

Key Concepts

  1. Sampling Methods: - Uniform sampling - Weighted sampling - Stratified sampling - Markov Chain Monte Carlo (MCMC)

  2. Quality Metrics: - Uniformity - Coverage - Diversity - Independence

  3. Performance Factors: - Sample generation time - Memory usage - Solution quality - Scalability

Model Sampling in Arlib

Please refer to alrib/sampling

class UniformSampler:
    def __init__(self, formula):
        self.formula = formula

    def sample(self, n):
        # Generate n uniform samples
        pass

References

  • Chakraborty, S., Meel, K. S., & Vardi, M. Y. (2013). A scalable approximate model counter. In International Conference on Principles and Practice of Constraint Programming.

  • Ermon, S., Gomes, C. P., & Selman, B. (2012). Uniform solution sampling using a constraint solver as an oracle. In Uncertainty in Artificial Intelligence.

  • Kitchen, N., & Kuehlmann, A. (2007). Stimulus generation for constrained random simulation. In IEEE/ACM International Conference on Computer-Aided Design.