60 research outputs found
A State-Space Perspective on Modelling and Inference for Online Skill Rating
This paper offers a comprehensive review of the main methodologies used for
skill rating in competitive sports. We advocate for a state-space model
perspective, wherein players' skills are represented as time-varying, and match
results serve as the sole observed quantities. The state-space model
perspective facilitates the decoupling of modeling and inference, enabling a
more focused approach highlighting model assumptions, while also fostering the
development of general-purpose inference tools. We explore the essential steps
involved in constructing a state-space model for skill rating before turning to
a discussion on the three stages of inference: filtering, smoothing and
parameter estimation. Throughout, we examine the computational challenges of
scaling up to high-dimensional scenarios involving numerous players and
matches, highlighting approximations and reductions used to address these
challenges effectively. We provide concise summaries of popular methods
documented in the literature, along with their inferential paradigms and
introduce new approaches to skill rating inference based on sequential Monte
Carlo and finite state-spaces. We close with numerical experiments
demonstrating a practical workflow on real data across different sports
Applied Metaheuristic Computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
Applied Methuerstic computing
For decades, Applied Metaheuristic Computing (AMC) has been a prevailing optimization technique for tackling perplexing engineering and business problems, such as scheduling, routing, ordering, bin packing, assignment, facility layout planning, among others. This is partly because the classic exact methods are constrained with prior assumptions, and partly due to the heuristics being problem-dependent and lacking generalization. AMC, on the contrary, guides the course of low-level heuristics to search beyond the local optimality, which impairs the capability of traditional computation methods. This topic series has collected quality papers proposing cutting-edge methodology and innovative applications which drive the advances of AMC
LIPIcs, Volume 261, ICALP 2023, Complete Volume
LIPIcs, Volume 261, ICALP 2023, Complete Volum
[Activity of Institute for Computer Applications in Science and Engineering]
This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in applied mathematics, fluid mechanics, and computer science
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