Investment and risk management problems are fundamental problems for  financial institutions and involve both speculative and hedging decisions. A structured approach to these problems naturally leads one to the field of applied mathematics in order to translate subjective probability beliefs and attitudes towards risk and reward into...

Performing Data Analysis Using IBM SPSS® uniquely addresses the presented statistical procedures with an example problem, detailed analysis, and the related data sets. Data entry procedures, variable naming,...

MATLAB is a high-level language and environment for numerical computation, visualization, and programming. Using MATLAB, you can analyze data, develop algorithms, and create models and applications. The language, tools, and built-in math functions enable you to explore multiple approaches and reach a solution faster than with spreadsheets or...

This volume first introduces the mathematical tools necessary for understanding and working with a broad class of applied stochastic models. The toolbox includes Gaussian processes, independently scattered measures such as Gaussian white noise and Poisson random measures, stochastic integrals, compound Poisson, infinitely divisible and stable...

Everyone encounters statistics on a daily basis. They are used in proposals, reports, requests, and advertisements, among others, to support assertions, opinions, and theories. Unless you’re a trained statistician, it can be bewildering. What are the numbers really saying or not saying? Better Business Decisions from Data:...

Introduction to Probability and Stochastic Processes with Applications presents a clear, easy-to-understand treatment of probability and stochastic processes, providing readers with a solid foundation they can build upon throughout their...

• Familiarize yourself with the main key performance indicators for game...

Between the Lines of Genetic Code lays out methodologies and tools for the measurement and evaluation of gene-gene and gene-environment studies and gives perspective on the future of this discipline. The book begins by defining terms for interaction studies, describing methodologies, and critically assessing the viability of current...

This edited volume on the latest advances in data science covers a wide range of topics in the context of data analysis and classification. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and various applications. The book gathers a selection...

• Get to grips with the application of Machine Learning methods using an extensive set of R packages
• Understand the benefits and potential pitfalls of using machine learning methods
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This book is a detailed, user-friendly guide to understanding and using Blender’s Python API for...