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Feng Li's Research Presentations
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2009-{SU; UU} - Flexible-modeling-of-conditional-distributions-using-smooth-mixtures-of-asymmetric-student-t-densities
2010-{ISBA} - Smooth-Mixtures-of-Asymmetric-Student-t-Densities
2011-{Linkoping; Hemavan} - Efficient-Bayesian-Multivariate-Surface-Regression
2013-{CUFE} - Bayesian-Modeling-of-Conditional-Densities
2013-{CUFE} - Spline-Methods
2013-{PKU} - Modeling-covariate-contingent-correlation-and-tail-dependence-with-copulas
2013-{RUC} - Introduction-to-covariate-dependent-copula-modeling
2013-{SU} - Complex-model-to-complex-data—-the-statistical-approach
2013-{Stockholm} - Bayesian-Multivariate-Density-Estimation
2013-{Stockholm} - Teaching-R-as-a-general-programming-language
2013-{Tianjing} - Bayesian-Modeling-of-Dependence-with-Copulas
2014-{CramerPrize} - Bayesian-Modeling-of-Conditional-Densities
2014-{Qvintensen} - Complex-Model-for-Complex-Data-via-the-Bayesian-Approach
2014-{Shandong} - Efficient-Bayesian-Response-Surface-Maximization
2014-{Tencent} - Logistic-Regression-with-Big-Data
2015-{CUFE} - Bayesian-Modeling-Tail-Dependence
2015-{IMSChina; Nanchang} - Dynamic-Tail-Dependence-and-Correlation-Modeling-with-Efficient-Bayesian-Approach
2016-{Hefei} - 统计分析与大数据分布式计算原理
2016-{ISBA} - Modelling-tail-dependence-with-stocks-and-financial-news-information
2016-{NationalLibrary} - 复杂模型应对复杂数据
2017-{CUFE} - 统计与大数据:工具与未来
2017-{Cairns; Tsinghua} - Improving-forecasting-performance-using-covariate-dependent-copula-models
2017-{EcoSta; IMS-China} - Bayesian-covariate-dependent-copula-modeling
2017-{RUC} - Complex-model-to-complex-data
2018-{Fudan; Nanjing} - Feature-based-time-series-generation-and-forecasting
2018-{Webinar} - Tool-Chain-for-Data-Science
2019-{ICSA} - Time-series-forecasting-based-on-automatic-feature-extraction
README.md

README.md

Feng Li's Research Presentations

This repository records Dr. Feng Li's recent research talks in conferences and other public occasions.

Some of the presentations were based ongoing projects at the time of presenting. Thus the results and findings may be biased and could be heavily revised in final published papers. Please refer to the final publications.

Cheers!

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