|"The 3rd Workshop on Statistical Modeling and Applications" 22-26/5|
Từ trang web Trường: http://web.hcmus.edu.vn/index.php?option=com_content&task=view&id=11370&Itemid=9
"The 3rd Workshop on Statistical Modeling and Applications"
BAYESIAN MODELS, INFERENCE AND
STATISTICAL DECISION MAKING
Time: 8:00 - 17:30, May 22nd - 26th, 2017 (Monday-Friday)
Place: Room I23, Building I
University of Science, VNU-HCM
227 Nguyen Van Cu, District 5, HCMC, Vietnam
● Module 1: "Unsupervised learning, Bayesian hierarchical models and Inference algorithms"
Lecturer: Nguyen Xuan Long, University of Michigan, Ann Arbor, USA
Abstract: In this lecture series I will present a number of basic and advanced topics in statistical modeling and algorithms, using clustering as a motivating problem. Clustering is a very basic and popular unsupervised learning problem in statistics and machine learning that has found numerous applications in many scientific and engineering fields. It also provides a concrete ground for us to talk about fundamental concepts in statistical inference. Such concepts include Bayesian hierarchical modeling, Markov Chain Monte Carlo and variational inference techniques. Advanced topics include Bayesian nonparametric models and algorithms.
List of tentative topics
(i) Clustering problems, K-means algorithms and statistical inference
(ii) Mixture models and the EM algorithm
(iii) Bayesian hierarchical models, variational inference and MCMC
(iv) Bayesian nonparametrics: models and algorithms
● Module 2: "Statistical Decision Theory and Bayesian Analysis"
Lecturer: Nabendu Pal, University of Louisiana, Lafayette, USA
Abstract: In this lecture, we will start at a very basic level, and then go all the way up to “Stein Effect” (Stein's Shrinkage Estimation). In the process, we'll have some applications, mostly in economics.
List of tentative topics:
(i) Basic setup of Statistical Decision Theory with the concepts of loss & risk functions;
(ii) Optimality criteria - Admissibility (Inadmissibility), Minimaxity;
(iii) The concept of a prior distribution, posterior distribution and finding the Bayes rule;
(iv) Connections among various optimal decision rules;
(v) Gibb's sampling, and how it is used in Bayesian analysis;
(vi) Stein's Identity, and its applications;
(vii) Stein's effect, Shrinkage estimation, and various extensions of shrinkage estimators.
● Module 3: "Rough Sets and Probabilistic Rough Set Models: Granular Computing on Approximations and Decision-Makings"
Lecturer: Dang Phuoc Huy, Dalat University, Vietnam
Abstract: Nowadays many rough sets based-approaches have been successfully applied in data mining, especially in research areas such as machine learning, knowledge discovery, decision analysis, and expert systems. The main aim of this lecture is to present the basic concepts of rough set theory and some special issues for using rough set models.
List of tentative topics
(i) Basic concepts and some results related to rough set based on the granularity
(ii) Popular rough set models and related problems
(iii) Decision-theoretic rough set models
(iv) Variable precision rough set models
SPONSOR: Faculty of Information Technology
University of Science
Vietnam National University, Ho Chi Minh City
This is the third event of a five-year program on "Statistical Modeling and Applications" proposed initially in 2015 by University of Science and University of Technology, VNU-HCM.
This year the workshop's theme focuses on "Bayesian Models, Inference, and Statistical Decision Making". More precisely, the workshop's topics will be on theory of rough set models, Bayesian analysis and models coupling with computational clustering, and last but not least the theory of statistical decision and advanced statistical inferences. Three topics all together present firm bonds and interesting interactions between statistical science with economics, computing (machine learning), together with optimal decision making theory in general.
Participants from all disciplines, especially STEM (Science, Technology, Engineering and Mathematics) areas are strongly encouraged to attend this multi flavored summer school. Researchers, lecturers, industrialists, business administrators, practitioners, managers and public policy makers would get immense benefits, in both theoretical knowledge and practical insights from this gathering.
Notice: During the workshop, Professor Nabendu Pal will be willing to discuss and possibly provide statistical consultancy on the realistic data sets that participants working with.
- Tran Dan Thu, University of Science, VNU-HCM
- Nguyen Dinh Thuc, University of Science, VNU-HCM
- Huynh Quang Vu, University of Science, VNU-HCM
- Van Chi Nam, University of Science, VNU-HCM
- Nguyen V. M. Man, University of Technology, VNU-HCM
- Nguyen An Khuong, University of Technology, VNU-HCM
- Luong Thi Hong Cam, Saigon University
- Giang Thuy Minh, Vietnam Journal of Science
TENTATIVE SCHEDULE: Download file
- Online Registration form: https://goo.gl/CCZ3PS
Registration Fee: 600K VND (for domestic) or 30USD (for international participants).
Bank transfer payment should be made by May 15th, 2017 (Monday) with detail notes consisting of [SMA 2017][Name of the Participant].+ For domestic participants
Please transfer your conference fee (600,000 VND) into one of the following bank accounts:
1. Account No: 0251002748081
2. Account No: 1606205922175
Please transfer your conference fee (30 USD) into the following bank account:
(Participants will obtain confirmation letter from the organising committee within 72 hours after the payment).
There will be a gala dinner for all participants, aimed for celebrating class survivors till the last day (!), starting 18:00 Friday evening, May 26, with fee 150K (VND) or 7 USD. If you are interested, you can ask at the register desk on May 22, and please make a payment in advance by Tuesday, May 23, 2017, for smooth arrangement.
- SMA Facebook Page: https://www.facebook.com/smatwp/
- Previous SMA events: