Decades ago, Paul Erdős used randomness to illuminate the vast and weird world of networks. Now mathematicians are making his ...

Abstract: This paper develops a probabilistic anticipation algorithm for dynamic objects observed by an autonomous robot in an urban environment. Predictive Gaussian mixture models are used due to ...

Objectives To quantify the preference of patients with diabetes mellitus (DM) for primary healthcare (PHC) institutions in China to redirect the patient flow and improve health outcomes. Design ...

Tzveta Iordanova is an expert in credit and risk management, financial reporting, and a writer for online financial services platforms. Andy Smith is a Certified Financial Planner (CFP®), licensed ...

Microsimulation is a form of individual-based stochastic simulation. In continuous time, microsimulation is closely related to discrete event simulation, and in discrete time it is closely related to ...

Probability theory is indispensable in computer science: It is at the core of artificial intelligence and machine learning, which require decision making under uncertainty. It is integral to CS theory ...

This Mini Review provides a focused and up-to-date summary of recent advancements in 3D discrete fracture network (DFN) modelling for simulating coupled thermo-hydro-mechanical-chemical processes in ...

to speed up the construction of the discrete-time model by turning to Newton-type methods for solving the Schrödinger system (in the next section, we show numerically that a mixed Newton–Sinkhorn ...