3 papers
cs.LO2026
Verifying Exact Samplers for Continuous Distributions with a Discrete Program Logic
Markus de Medeiros, Puming Liu, Kwing Hei Li +3
Most implementations of sampling algorithms for continuous distributions use floating-point numbers, which introduce round-off errors and approximations. These errors can be diffic…
cs.LO2024
Error Credits: Resourceful Reasoning about Error Bounds for Higher-Order Probabilistic Programs
Alejandro Aguirre, Philipp G. Haselwarter, Markus de Medeiros +4
Probabilistic programs often trade accuracy for efficiency, and thus may, with a small probability, return an incorrect result. It is important to obtain precise bounds for the pro…
cs.LO2024
Tachis: Higher-Order Separation Logic with Credits for Expected Costs
Philipp G. Haselwarter, Kwing Hei Li, Markus de Medeiros +4
We present Tachis, a higher-order separation logic to reason about the expected cost of probabilistic programs. Inspired by the uses of time credits for reasoning about the running…