Simulation-based inference (SBI) methods such as approximate Bayesian computation
(ABC), synthetic likelihood, and neural posterior estimation (NPE) rely on simulating …

Simulator-based models are models for which the likelihood is intractable but simulation of
synthetic data is possible. They are often used to describe complex real-world phenomena …

Likelihood-free inference methods typically make use of a distance between simulated and
real data. A common example is the maximum mean discrepancy (MMD), which has …

Millimeter-wave (MmWave) channel characteristics are quite different from sub-6 GHz
frequency bands. The major differences include higher path loss and sparser multipath …

Model misspecification can create significant challenges for the implementation of
probabilistic models, and this has led to development of a range of robust methods which …

Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for
models with intractable likelihood functions. As ABC methods usually rely on comparing …

Intractable generative models, or simulators, are models for which the likelihood is
unavailable but sampling is possible. Most approaches to parameter inference in this setting …

Stochastic radio channel models based on underlying point processes of multipath
components (MPCs) have been studied intensively since the seminal papers of Turin and …

Simulation-based inference (SBI) is the preferred framework for estimating parameters of
intractable models in science and engineering. A significant challenge in this context is the …

This paper proposes a novel graph-learning-based radio propagation model, referred to as
propagation graph representation learning. Recent advancements in deep learning have …