Stochastic Modeling
Nicolas Lanchier
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Description for Stochastic Modeling
Paperback. Series: Universitext. Num Pages: 303 pages, 57 black & white illustrations, 6 colour illustrations, biography. BIC Classification: PBT; PBWL. Category: (P) Professional & Vocational. Dimension: 161 x 241 x 19. Weight in Grams: 512.
Three coherent parts form the material covered in this text, portions of which have not been widely covered in traditional textbooks. In this coverage the reader is quickly introduced to several different topics enriched with 175 exercises which focus on real-world problems. Exercises range from the classics of probability theory to more exotic research-oriented problems based on numerical simulations. Intended for graduate students in mathematics and applied sciences, the text provides the tools and training needed to write and use programs for research purposes.
The first part of the text begins with a brief review of measure theory and revisits the ... Read more
Product Details
Format
Paperback
Publication date
2017
Publisher
Springer International Publishing AG Switzerland
Number of pages
303
Condition
New
Series
Universitext
Number of Pages
303
Place of Publication
Cham, Switzerland
ISBN
9783319500379
SKU
V9783319500379
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Nicolas Lanchier
Nicolas Lanchier is Associate Professor at Arizona State University, School of Mathematical and Statistical Sciences. His research interests include mathematical models introduced in the life and social sciences literature that describe inherently spatial phenomena of interacting populations consist of systems of ordinary differential equations.
Reviews for Stochastic Modeling
“Stochastic Modeling by Nicolas Lanchier is an introduction to stochastic processes accessible to advanced students and interdisciplinary scientists with a background in graduate-level real analysis. The work offers a rigorous approach to stochastic models used in social, biological and physical sciences ... . Stochastic modeling provides a link between applied research in stochastic models and the literature covering their mathematical ... Read more