National Seminar on Mathematical and Bayesian Statistical Modeling for Engineering applications: Models, Analysis and Applications

ABOUT THE SEMINAR
A model is a representation or an abstraction of a system or a process. We build models because they help us to define our problems, organize our thoughts, understand our data, communicate and test that understanding and make predictions. One of the most important aims for construction of models is to define the problem such that only important details becomes visible, while irrelevant features are neglected. A mathematical model is a description of a system using mathematical concepts and language. Mathematical modeling is the art of translating problems from an application area into tractable mathematical formulations whose theoretical and numerical analysis provides insight, answers and guidance useful for the originating application.
Mathematical statistics uses two major paradigms, conventional and Bayesian. Bayesian methods reduce statistical inference to problems in probability theory, thereby minimizing the need for completely new concepts, and serve to discriminate among conventional statistical techniques, by either providing a logical justification to some or proving the logical inconsistency of others.
Bayesian inference has applications in artificial intelligence and expert systems. There is also an ever growing connection between Bayesian methods and simulation-based Monte Carlo techniques since complex models cannot be processed in closed form by a Bayesian analysis, while a graphical model structure may allow for efficient simulation algorithms like the Gibbs sampling.
Recently Bayesian inference has gained popularity amongst the phylogenetics community for these reasons; a number of applications allow many demographic and evolutionary parameters to be estimated simultaneously. As applied to statistical classification, Bayesian inference has been used in recent years to develop algorithms for identifying e-mail spam. Applications which make use of Bayesian inference for spam filtering include CRM114, DSPAM, Bogofilter, SpamAssassin, SpamBayes, Mozilla, XEAMS and others.
The main aim of the seminar is to collaborate mathematicians, computer scientists, physicists, statisticians, operations research analysts, economists and engineers.
COURSE TOPICS Thinking with Mathematical Models : Simple to Complex Real Problems Bayesian statistical modeling-Analysis Mathematical and Bayesian Modeling – Engineering Applications

COURSE TOPICS
Thinking with Mathematical Models : Simple to Complex Real Problems

Bayesian statistical modeling-Analysis

Mathematical and Bayesian Modeling – Engineering Applications

Dr.S.Sundar Professor,
Department of Mathematics
Indian Institute of Technology Madras Chennai

Dr.P.Senthilkumar Assistant Professor, Department of Mathematics
Govt. Arts & Science College, Kangeyam

Dr.R.Thangarajan Professor, Department of CSE, KEC

SCHEDULED DATES
Last date for receipt of Applications : 20.10.2017 Intimation to the Participants : 24.10.2017

Demand Draft for Rs.200/- drawn in favour of “KONGU ENGINEERING COLLEGE GRANTS” payable at Perundurai.

The college is located at Perundurai on the National Highway (NH 47) about 80 kms from Coimbatore and 20 kms from Erode. It is well connected by road & rail.

BOARDING AND LODGING
Boarding and lodging will be provided for all the participants in the college campus on a chargeable basis. Participants are required to make arrangements on their own for those who accompany them, if any.