Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2010)

Venue: Panaorama Hotel

Location: Chania, Crete, Greece

Event Date/Time: Jun 08, 2010 End Date/Time: Jun 11, 2010
Early Registration Date: Mar 30, 2010
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The main goal of the Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2010) is to promote new methods and techniques for analyzing data, in fields like stochastic modeling, optimization techniques, statistical methods and inference, data mining and knowledge systems, computing-aided decision supports, neural networks and chaotic data analysis.
SMTDA aims at bringing together people from both stochastic and data analysis areas. Special attention is given to applications or to new theoretical results having potential of solving real life problems.
The Conference includes topics as: Stochastic Processes and Models, Distributions, Insurance, Stochastic Modelling for Healthcare Management, Markov and Semi Markov models, Parametric/ Non -Parametric, Dynamical Systems/Forecasting, Modeling and Chaotic Modeling, Sampling and Optimization problems, Control problems and Neural Networks, Data Mining, Clustering and Classification, Applications of Data Analysis and various other applications

The conference includes also 2 Workshops on I) Lifetime Data and II) Innovation Diffusion Modeling. You can also send Abstracts and participate in these events (Email: )

Publications in Books from Previous Conferences which we have organized in Chania on Stochastic Modeling and Data Analysis.

1. Janssen, J. and Skiadas, C. H. (Eds): Applied Stochastic Models and Data Analysis, Vol. I and II, World Scientific, 1993, 1139 pages.
2. Janssen, J., Skiadas, C. H. and Zopounidis, C. (Eds): Advances in Stochastic Modelling and Data Analysis, Kluwer, 1995 (now published by Springer)
( ).
3. Skiadas, C. H. (Ed): Recent Advances in Stochastic Modeling and Data Analysis, World Scientific, 2007 ( ).
4. Skiadas, C. H. (Ed): Advances in Data Analysis: Theory and Applications to Reliability and Inference, Data Mining, Bioinformatics, Lifetime Data, and Neural Networks, Springer/Birkhauser, Boston, 2010, ).