This paper presents a quantitative program verification infrastructure for discrete
probabilistic programs. Our infrastructure can be viewed as the probabilistic analogue of …

This paper presents a program analysis method that generates program summaries
involving polynomial arithmetic. Our approach builds on prior techniques that use solvable …

Loop invariants are properties of a program loop that hold before and after each iteration of
the loop. They are often employed to verify programs and ensure that algorithms consistently …

We study the problem of bounding the posterior distribution of discrete probabilistic
programs with unbounded support, loops, and conditioning. Loops pose the main difficulty in …

Many stochastic continuous-state dynamical systems can be modeled as probabilistic
programs with nonlinear non-polynomial updates in non-nested loops. We present two …

Probabilistic programming is an emerging paradigm enabling software developers to model
uncertainty of real data and to support suitable inference operations directly into computer …

The automatic generation of loop invariants is a fundamental challenge in software
verification. While this task is undecidable in general, it is decidable for certain restricted …

Automatic generation of loop invariants is a fundamental challenge in software verification.
While this task is undecidable in general, it is decidable for certain restricted classes of …

Automatically generating invariants, key to computer-aided analysis of probabilistic and
deterministic programs and compiler optimisation, is a challenging open problem. Whilst the …

Automatic static cost analysis infers information about the resources used by programs
without actually running them with concrete data and presents such information as functions …