基本信息
浏览量:17
职业迁徙
个人简介
About me
Kostas’ research focuses on developing and applying advanced computational methods, such as Markov Chain and Sequential Monte Carlo, for Bayesian Inference. His methodology has mostly targeted continuous time probability models based on stochastic differential equations driven by standard or fractional Brownian motion. The areas of application include Financial and Econometric Time Series as well biomedical problems such as stochastic epidemic models and analysis of growth curves.
Kostas’ research focuses on developing and applying advanced computational methods, such as Markov Chain and Sequential Monte Carlo, for Bayesian Inference. His methodology has mostly targeted continuous time probability models based on stochastic differential equations driven by standard or fractional Brownian motion. The areas of application include Financial and Econometric Time Series as well biomedical problems such as stochastic epidemic models and analysis of growth curves.
研究兴趣
论文共 16 篇作者统计合作学者相似作者
按年份排序按引用量排序主题筛选期刊级别筛选合作者筛选合作机构筛选
时间
引用量
主题
期刊级别
合作者
合作机构
arXiv (Cornell University) (2023)
加载更多
作者统计
合作学者
合作机构
D-Core
- 合作者
- 学生
- 导师
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn