1 citations · 1 across the 5 of their papers we have counts for
5 papers
Polynomial Invariants for Probabilistic Transition Systems with Unbounded Support
Anne Schreuder, Lorenz Winkler, Laura Kovács +1
We study the synthesis of polynomial invariants for probabilistic transition systems (PTS) based on martingale theory. We present tractable methods to verify that such polynomials…
LazyHMC: Hamiltonian Monte Carlo Simulation for Lazy, Infinite Dimensional Probabilistic Programs
Maria-Nicoleta Crăciun, C. -H. Luke Ong, Tom Schrijvers +1
Hamiltonian Monte Carlo (HMC) is a successful generic inference method in probabilistic programming, but in its ordinary formulation it needs gradients and finite-dimensional param…
Quantitative Verification of Omega-regular Properties in Probabilistic Programming
Peixin Wang, Jianhao Bai, Min Zhang +1
Probabilistic programming provides a high-level framework for specifying statistical models as executable programs with built-in randomness and conditioning. Existing inference tec…
Unifying Qualitative and Quantitative Safety Verification of DNN-Controlled Systems
Dapeng Zhi, Peixin Wang, Si Liu +2
The rapid advance of deep reinforcement learning techniques enables the oversight of safety-critical systems through the utilization of Deep Neural Networks (DNNs). This underscore…
Static Posterior Inference of Bayesian Probabilistic Programming via Polynomial Solving
Peixin Wang, Tengshun Yang, Hongfei Fu +2
In Bayesian probabilistic programming, a central problem is to estimate the normalised posterior distribution (NPD) of a probabilistic program with conditioning via score (a.k.a. o…