National Seminar on
Mathematical and Bayesian Statistical Modeling for Engineering applications:
Models, Analysis and Applications, Kongu Engineering college , Erode, 27th
October 2017.
College name : Kongu Engineering College.
Event Date : 27th October
2017
Last Date to Register :
Last date for receipt of
Applications: 20th October 2017
Intimation
to the Participants: 24th October 2017
Address : Erode, Tamil Nadu.
Contact Mail Address : kecmathematics@gmail.com
Events
List :
Thinking with Mathematical Models : Simple to Complex Real Problems
Bayesian statistical modeling-Analysis
Mathematical and Bayesian Modeling – Engineering Applications
About
Event :
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
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
Accommodation :
Accommodation will be Provided
in the college itself as per the Participants required .
For More
Contact :
Dr. M. Dhavamani,
Department
of Mathematics
School of
Science & Humanities
Kongu
Engineering College Perundurai,
Erode –
638060, Tamil Nadu.
Mobile:
9842740601
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