Conveners
Statistical/Stochastic Modelling
- David Steinberg (Tel Aviv University)
Statistical/Stochastic Modelling: 2
- Froydis Bjerke (Animalia Meat and Poultry Research Centre)
Statistical/Stochastic Modelling: 3
- Antonio Pievatolo (CNR-IMATI)
Statistical/Stochastic Modelling: 4
- Nikolaus Haselgruber (CIS Consulting in Industrial Statistics GmbH)
The EU Digital Decade Policy Programme 2030 strongly depends on safe and reliable cutting-edge technologies, like Micro-Electro-Mechanical Systems (MEMS) sensors, that are widely used in large sensor networks for infrastructural, environmental, healthcare, safety, automotive, energy and industrial monitoring. The massive production of these sensors, often in the order of millions per week,...
Estimating traffic volumes across street networks is a critical step toward enhancing transport planning and implementing effective road safety measures.
Traditional methods for obtaining traffic data rely on manual counts or high-precision automatic sensors (e.g., cameras or inductive loops). While manual counting is labor-intensive and time-consuming, fixed sensors are costly and typically...
The advent of Industry 5.0—characterized by its emphasis on resilient and sustainable technology integration—aims to reorient industrial production toward a more competitive model with a positive societal impact. Within this framework, the Joint Research Unit (CEMI) formed by the shipbuilding company Navantia and the Universidade da Coruña is focused on developing and validating advanced...
In the planning of order picking systems, which are characterized by an increasing complexity as well as uncertainties, discrete-event simulation is widely used. It enables investigations of systems using experiments based on executable models. However, the execution of simulation experiments with different parameter configurations (simulation runs) is associated with a high level of effort....
In today’s industrial landscape, effective decision-making increasingly relies on the ability to assess target ordinal variables - such as the degree of deterioration, quality level, or risk stage of a process - based on high-dimensional sensor data. In this regard, we tackle the problem of predicting a ordinal variable based on observable features consisting of functional profiles, by...
Growth curves are essential tools in biology for tracking changes in population size or biomass over time. Biological growth usually follows a sigmoid pattern, characterized by an initial slow growth (lag phase), a rapid increase (exponential or log phase), and a leveling off as they approach mature values (stationary phase and plateau). Commonly used growth models include the logistic model,...
Mixed effect regression models are statistical models that not only contain fixed effects but also random effects. Fixed effects are non-random quantities, while random effects are random variables. Both of these effects must be estimated from data. A popular method for estimating mixed models is restricted maximum likelihood (REML).
The Julia programming language already has a...
In reservation-based services with volatile demand and competitive pricing pressures, dynamically optimizing prices is essential for revenue maximization. This paper introduces a data-driven pricing framework that integrates demand forecasting with stochastic optimization. We model customer arrivals using a non-homogeneous Poisson process, where expected demand is estimated through a Poisson...
This paper presents a novel framework for designing adaptive testing procedures by leveraging the properties of waiting-time distributions. The proposed approach integrates temporal information - specifically, the time needed for a specific sequence of correct answers to be realized—into the testing process, enabling a more dynamic and individualized assessment of examinee performance. By...
Calibrating a simulation model involves estimating model's parameters by comparing its outputs with experiences to ensure that simulation results accurately reflect those experiences. However, when outputs are functions of time, there are multiple ways to define the difference between experimental and simulated outputs. It has recently been proposed to use elastic functional data analysis,...
Innovation diffusion phenomena have long attracted researchers due to their interdisciplinary nature, which allows for integrating theories and concepts from various fields, including natural sciences, mathematics, physics, statistics, social sciences, marketing, economics, and technological forecasting. The formal representation of diffusion processes has historically relied on epidemic...
In recent decades, numerical experimentation has established itself as a valuable and cost-effective alternative to traditional field trials for investigating physical phenomena and evaluating the environmental impact of human activities. Nevertheless, high-fidelity simulations often remain computationally prohibitive due to the detailed modelling required and the complexity of parameter...