We present a novel static analysis technique to derive higher moments for program
variables for a large class of probabilistic loops with potentially uncountable state spaces …

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

This paper presents alternative techniques for inference on classical Bayesian networks in
which all probabilities are fixed, and for synthesis problems when conditional probability …

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

Bayesian networks (BNs) are widely used for knowledge representation and reasoning.
However, they suffer from the following limitations: 1) They are unable to explicitly learn …

We present an exact approach to analyze and quantify the sensitivity of higher moments of
probabilistic loops with symbolic parameters, polynomial arithmetic and potentially …

We present a quantum inference algorithm for discrete Bayesian networks using quantum
rejection sampling and a quantum circuit construction method to deal with conditional …

Verifying probabilistic programs requires reasoning about various probabilistic behaviors,
eg, random sampling, nondeterminism, and conditioning, against multiple quantitative …

We present the Polar framework for fully automating the analysis of classical and
probabilistic loops using algebraic reasoning. The central theme in Polar comes with …

A probabilistic loop is a programming control flow structure whose behavior depends on
random variables' assignments and probabilistic conditions. One challenging problem is …