Quantum annealing (QA) is a heuristic quantum optimization algorithm that can be used to
solve combinatorial optimization problems. In recent years, advances in quantum …

Abstract Bayesian Networks (BNs) have become increasingly popular over the last few
decades as a tool for reasoning under uncertainty in fields as diverse as medicine, biology …

In Bayesian structure learning, we are interested in inferring a distribution over the directed
acyclic graph (DAG) structure of Bayesian networks, from data. Defining such a distribution …

Learning a faithful directed acyclic graph (DAG) from samples of a joint distribution is a
challenging combinatorial problem, owing to the intractable search space superexponential …

Physics-informed neural networks (PINNs) have recently emerged as an alternative way of
numerically solving partial differential equations (PDEs) without the need of building …

Bayesian Networks are probabilistic graphical models that represent conditional
independence relationships among variables as a directed acyclic graph (DAG), where …

A valuable overview of the most important ideas and results in statistical modeling Written by
a highly-experienced author, Foundations of Linear and Generalized Linear Models is a …

The method of model averaging has become an important tool to deal with model
uncertainty, for example in situations where a large amount of different theories exist, as are …

When jobs offered by different employers are not perfect substitutes, employers gain wage-
setting power; the extent of this power can be captured by the elasticity of labor supply to the …

Bayesian model averaging (BMA) provides a coherent and systematic mechanism for
accounting for model uncertainty. It can be regarded as an direct application of Bayesian …