JPhysA特刊征稿|Bayesian Statistics for Complex Systems

特刊详情

客座编辑

• 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

主题范围

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.

投稿流程

特刊文章与JPhysA期刊常规文章遵循相同的审稿流程和内容标准，并采用同样的投稿模式。

有关准备文章及投稿的详细信息，可以参阅IOPscience页面的作者指南。

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”。

投稿截止日期：2024年10月31日。