Proceedings of the 2nd International Conference on Statistics: Theory and Applications最新文献

Modelling multiscale collective behavior with Gaussian processes 用高斯过程模拟多尺度集体行为

Proceedings of the 2nd International Conference on Statistics: Theory and Applications Pub Date : 2020-08-01 DOI: 10.11159/icsta20.124

Nazareno Campioni, D. Husmeier, J. Morales, J. Gaskell, C. Torney

引用次数: 0

Closed-loop effects in cardiovascular clinical decision support 心血管临床决策支持的闭环效应

Proceedings of the 2nd International Conference on Statistics: Theory and Applications Pub Date : 2020-08-01 DOI: 10.11159/icsta20.128

D. Husmeier, L. Paun

引用次数: 1

Comparative Evaluation of Different Emulators for Cardiac Mechanics 心脏力学仿真器的比较评价

Proceedings of the 2nd International Conference on Statistics: Theory and Applications Pub Date : 2020-08-01 DOI: 10.11159/icsta20.126

D. Dalton, Alan Lazarus, D. Husmeier

引用次数: 2

Approximate Bayesian Inference for Individual-based Models with Emergent Dynamics 具有涌现动力学的基于个体模型的近似贝叶斯推理

Proceedings of the 2nd International Conference on Statistics: Theory and Applications Pub Date : 2020-08-01 DOI: 10.11159/icsta20.125

J. Gaskell, Nazareno Campioni, J. Morales, D. Husmeier, C. Torney

{"title":"Approximate Bayesian Inference for Individual-based Models with Emergent Dynamics","authors":"J. Gaskell, Nazareno Campioni, J. Morales, D. Husmeier, C. Torney","doi":"10.11159/icsta20.125","DOIUrl":"https://doi.org/10.11159/icsta20.125","url":null,"abstract":"Individual-based models are used in a variety of scientific domains to study systems composed of multiple agents that interact \u0000with one another and lead to complex emergent dynamics at the macroscale. A standard approach in the analysis of these systems is \u0000to specify the microscale interaction rules in a simulation model, run simulations, and then qualitatively compare outputs to empirical \u0000observations. Recently, more robust methods for inference for these types of models have been introduced, notably approximate Bayesian \u0000computation, however major challenges remain due to the computational cost of simulations and the nonlinear nature of many complex \u0000systems. Here, we compare two methods of approximate inference in a classic individual-based model of group dynamics with well-studied \u0000nonlinear macroscale behaviour; we employ a Gaussian process accelerated ABC method with an approximated likelihood and with a \u0000synthetic likelihood. We compare the accuracy of results when re-inferring parameters using a measure of macro-scale disorder (the \u0000order parameter) as a summary statistic. Our findings reveal that for a canonical simple model of animal collective movement, parameter \u0000inference is accurate and computationally efficient, even when the model is poised at the critical transition between order and disorder.","PeriodicalId":302827,"journal":{"name":"Proceedings of the 2nd International Conference on Statistics: Theory and Applications","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122692369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1