報告題目:An Optimal Composite Likelihood Estimation and Prediction for Large-scale Gaussian Process Models

報告所屬學科：管理科學與工程

報告人：李勇祥(上海交通大學)

報告時間：2019年12月25日 15:00

報告地點：將軍路校區經管樓401室

報告摘要：

Large-scale Gaussian process (GP) models are becoming increasingly important and popularly used in the context of geostatistics, machine learning, simulation optimization, etc. However, the standard methods of GP models, the maximum likelihood estimation and the best linear unbiased predictor, are designed to run in a single computer whose computational power is often limited even for a computer in a super-computing center. Therefore, approximate alternatives that can use the power of multiple computers are in an increasing demand, such as the composite likelihood methods. However, those alternative methods in the literature offer limited options for practitioners, because most methods care more about computational efficiency than the statistical efficiency. In fact, there is lack of methods in the literature that can provide accurate solutions to parameter estimation and model prediction of large-scale GP applications for practitioners who can use a super-computing center. Therefore, we develop an optimal composite likelihood method in this paper that tries to minimize information loss in parameter estimation and prediction of large-scale GP models. We prove that the proposed composite likelihood prediction, called the best linear unbiased block predictor, has the minimum prediction variance under some conditions. Numerical examples show that both the composite parameter estimation and prediction method we proposed exhibit more accurate performance than their traditional counterparts under various cases.

報告人簡介：

李勇祥博士現為上海交通大學工業工程與管理系助理教授，于2019年在香港城市大學數據學科學院獲得博士學位。他主要從事數據科學在復雜系統的優化設計，質量控制和故障檢測中的不確定性研究，主要研究方向包括計算機試驗設計與分析，統計質量控制，統計信號處理等。他在統計和工業工程領域著名期刊Technometrics、IEEE Transactions on Signal Processing等發表多篇論文。