Математика
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- Applied Linear Regression
- Evolutionary Optimization Algorithms
- Time Series Analysis
- Simulation Modeling and Arena
- Beginning Partial Differential Equations
- Fundamentals of Actuarial Mathematics
- Probability and Stochastic Processes
- The Intelligent Enterprise in the Era of Big Data
- Matrix Algebra for Linear Models
- Computational Approaches to Studying the Co-evolution of Networks and Behavior in Social Dilemmas
- Introduction to Stochastic Processes with R
- Basic Data Analysis for Time Series with R
- A First Course in Mathematical Logic and Set Theory
- Multivariate Nonparametric Regression and Visualization. With R and Applications to Finance
- Advances in DEA Theory and Applications. With Extensions to Forecasting Models
- An Introduction to SAGE Programming. With Applications to SAGE Interacts for Numerical Methods
- Spline Collocation Methods for Partial Differential Equations. With Applications in R
- Mathematical and Computational Modeling. With Applications in Natural and Social Sciences, Engineering, and the Arts
- Banking Systems Simulation. Theory, Practice, and Application of Modeling Shocks, Losses, and Contagion
- Mixed Models. Theory and Applications with R
- The Data Industry. The Business and Economics of Information and Big Data
- Nonlinear Parameter Optimization Using R Tools
- Beyond Basic Statistics. Tips, Tricks, and Techniques Every Data Analyst Should Know
- Introduction to Probability and Statistics for Ecosystem Managers. Simulation and Resampling
- Elements of Random Walk and Diffusion Processes
- Probabilities. The Little Numbers That Rule Our Lives
- Time Series Analysis. Nonstationary and Noninvertible Distribution Theory
- Approaches to Geo-mathematical Modelling. New Tools for Complexity Science
- Business Risk Management. Models and Analysis
- First Hitting Time Regression Models. Lifetime Data Analysis Based on Underlying Stochastic Processes
- Optimierung in C++. Grundlagen und Algorithmen
- Markov Chains. From Theory to Implementation and Experimentation
- Data Mining Algorithms. Explained Using R
- Constraint Satisfaction Problems. CSP Formalisms and Techniques
- Global Dynamics. Approaches from Complexity Science
- Markov Chains. Analytic and Monte Carlo Computations
- Introductory Statistics and Analytics. A Resampling Perspective
- Principles of Mathematics. A Primer
- Clinical Trials. A Methodologic Perspective
- Introductory Modern Algebra. A Historical Approach
- Theory of Probability. A critical introductory treatment
- Combinatorics. An Introduction
- Bayesian Methods for Management and Business. Pragmatic Solutions for Real Problems
- Differential Equation Analysis in Biomedical Science and Engineering. Partial Differential Equation Applications with R
- Applied Mathematics for the Analysis of Biomedical Data. Models, Methods, and MATLAB
- Efficiency and Productivity Growth. Modelling in the Financial Services Industry
- Solutions Manual to Accompany Linear Algebra. Ideas and Applications
- Linear Algebra. Ideas and Applications
Всего страниц: 134