BEGIN:VCALENDAR VERSION:2.0 PRODID:-//KnowAFest//Campus Events//EN BEGIN:VEVENT UID:eventsphere-49942@www.knowafest.com DTSTAMP:20261008T144621Z DTSTART:20171026T183000Z DTEND:20171027T182959Z SUMMARY:National Seminar on Mathematical and Bayesian Statistical Modeling for Engineering applications: Models\, Analysis and Applications LOCATION:Kongu Engineering College\, Erode DESCRIPTION:

ABOUT THE SEMINAR
\nA model is a representation or an a bstraction of a system or a process. We build models because they help us to define our problems\, organize our thoughts\, understand our data\, com municate and test that understanding and make predictions. One of the most important aims for construction of models is to define the problem such t hat only important details becomes visible\, while irrelevant features are neglected. A mathematical model is a description of a system using mathem atical concepts and language. Mathematical modeling is the art of translat ing problems from an application area into tractable mathematical formulat ions whose theoretical and numerical analysis provides insight\, answers a nd guidance useful for the originating application.
\nMathematical st atistics uses two major paradigms\, conventional and Bayesian. Bayesian me thods reduce statistical inference to problems in probability theory\, the reby minimizing the need for completely new concepts\, and serve to discri minate among conventional statistical techniques\, by either providing a l ogical justification to some or proving the logical inconsistency of other s.
\nBayesian inference has applications in artificial intelligence a nd expert systems. There is also an ever growing connection between Bayesi an methods and simulation-based Monte Carlo techniques since complex model s cannot be processed in closed form by a Bayesian analysis\, while a grap hical model structure may allow for efficient simulation algorithms like t he Gibbs sampling.
\nRecently Bayesian inference has gained popularit y amongst the phylogenetics community for these reasons\; a number of appl ications allow many demographic and evolutionary parameters to be estimate d simultaneously. As applied to statistical classification\, Bayesian infe rence has been used in recent years to develop algorithms for identifying e-mail spam. Applications which make use of Bayesian inference for spam fi ltering include CRM114\, DSPAM\, Bogofilter\, SpamAssassin\, SpamBayes\, M ozilla\, XEAMS and others.
\nThe main aim of the seminar is to collab orate mathematicians\, computer scientists\, physicists\, statisticians\, operations research analysts\, economists and engineers.
\nCOURSE TOP ICS Thinking with Mathematical Models : Simple to Complex Real Problems Ba yesian statistical modeling-Analysis Mathematical and Bayesian Modeling – Engineering Applications

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