Probabilistic programming languages (PPLs) are an expressive means of representing and
reasoning about probabilistic models. The computational challenge of probabilistic …

We consider the problem of expected cost analysis over nondeterministic probabilistic
programs, which aims at automated methods for analyzing the resource-usage of such …

We propose a symbolic execution method for programs that can draw random samples. In
contrast to existing work, our method can verify randomized programs with unknown inputs …

Despite the successes of probabilistic models based on passing noise through neural
networks, recent work has identified that such methods often fail to capture tail behavior …

We revisit two well-established verification techniques, k-induction and bounded model
checking (BMC), in the more general setting of fixed point theory over complete lattices. Our …

We present a novel methodology for the automated resource analysis of non-deterministic,
probabilistic imperative programs, which gives rise to a modular approach. Program …

In this work, we consider the almost-sure termination problem for probabilistic programs that
asks whether a given probabilistic program terminates with probability 1. Scalable …

We propose a new method to approximate the posterior distribution of probabilistic
programs by means of computing guaranteed bounds. The starting point of our work is an …

Morgan and McIver's weakest pre-expectation framework is one of the most well-established
methods for deductive verification of probabilistic programs. Roughly, the idea is to …

Probabilistic programs extend classical imperative programs with random-value generators.
For classical non-probabilistic programs, termination is a key question in static analysis of …