Advances in Statistical Simulation and Computing, 2nd Edition

Dear Colleagues,

This Special Issue represents a continuation and expansion of the successful first edition of “Advances in Statistical Simulation and Computing”, aiming to provide a more consolidated international forum for methodological, computational, and applied advances in modern statistical simulation.

Statistical simulation and computing have become central pillars of contemporary statistical research and data-driven science. Simulation-based approaches allow researchers to address complex, high-dimensional, stochastic, and computationally demanding problems that are analytically intractable or impractical to solve using classical methods. In particular, sampling-based techniques—such as Monte Carlo methods and their extensions—play a fundamental role in exploring probabilistic models, assessing uncertainty, and supporting statistical inference across a wide range of disciplines.

In recent years, the field has experienced rapid growth driven by several converging developments: the increasing availability of high-performance computing (HPC) resources; the emergence of parallel, distributed, and graphics processing unit (GPU)-based computing architectures; advances in probabilistic programming; and the growing interaction between statistical simulation, Bayesian computation, and machine learning. These developments have enabled the design and implementation of increasingly sophisticated algorithms, significantly improving computational efficiency, scalability, and practical applicability.

This second edition seeks to broaden the thematic scope of the Special Issue while reinforcing its original core. We welcome original research articles and high-quality review papers that contribute to theoretical advances, methodological innovations, and applied developments in statistical simulation and computing. Particular emphasis is placed on contributions that bridge theory and practice, introduce reproducible computational frameworks, or demonstrate the impact of simulation-based methods in real-world and interdisciplinary applications.

Topics of interest include, but are not limited to:

• Monte Carlo methods and advanced stochastic simulation techniques;
• Simulation-based inference, including Approximate Bayesian Computation (ABC), likelihood-free methods, and simulation-based calibration;
• Bayesian computation, including Markov chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC) methods;
• High-dimensional and hierarchical models, latent variable models, and complex dependency structures;
• Simulation of stochastic processes, including stochastic differential equations and point processes;
• Spatio-temporal and space–time simulation models;
• Computational statistics and algorithmic advances for large-scale data analysis;
• Parallel, concurrent, distributed, and GPU-based computing applied to statistical simulation;
• Probabilistic programming and computational frameworks for statistical modeling;
• Interfaces between statistical simulation and machine learning, including simulation methods for deep and generative models;
• Reproducible research, open-source software, and benchmarking studies in statistical simulation;
• Applications in health sciences, environmental and climate modeling, engineering, risk analysis, and uncertainty quantification.

By expanding the thematic focus of the first edition and incorporating recent methodological and computational advances, this Special Issue aims to provide a comprehensive and forward-looking perspective on the future of statistical simulation and computing.

Dr. Francisco Novoa-Muñoz
Dr. Bernardo M. Lagos-Álvarez
Guest Editors

Manuscript Submission Information

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