JPhysA特刊征稿|Bayesian Statistics for Complex Systems

15 4月 2024 gabriels



  • Michael A. Lomholt,丹麦南丹麦大学
  • Ralf Metzler,德国波茨坦大学
  • Samudrajit Thapa,德国马克斯·普朗克复杂系统物理研究所


The physical understanding of complex systems poses major challenges due to factors such as large numbers of system variables, many of which might be interdependent; memory effects and heterogeneity in the system; lack of feasibility to repeat experiments and limited sampling of the data space often in the presence of one or more sources of noise. Bayesian statistics provides a framework for parameter estimation, model comparison and uncertainty quantification which has proven and continues to be useful in overcoming many of these challenges.

This issue will feature theoretical and/or computational studies that focus on the development and/or application of Bayesian methods as a data-driven approach to model complex systems across several fields including soft matter, climate, movement ecology, biology and epidemiology. The issue is intended to provide a platform for exchanges within and between diverse scientific communities which share a common interest in Bayesian statistics.





作者可登入期刊主页进行在线投稿,先选择“文章类型”,然后在“选择特刊”的下拉框中选择“Bayesian Statistics for Complex Systems”。



Journal of Physics A: Mathematical and Theoretical

  • 2022年影响因子:2.1  Citescore:4
  • Journal of Physics A: Mathematical and Theoretical(JPhysA)每年出版50期,针对运用数学结构来描述物理世界的基本过程,并探索这些结构的分析、计算和数值方法。期刊内容涵盖:统计物理;非平衡系统、计算方法和现代平衡理论;混沌和复杂系统;数学物理;量子力学和量子信息理论;场论和弦理论;流体和等离子体理论;生物模型等方面。文章类型包括原创性论文和综述,以及关注于热点研究的专题综述和特刊,提供及时、全面的纵览。