Learn IT - Books tags parametric

MATLAB Linear Algebra

Apress, 2014

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...

MATLAB Graphical Programming: Practical hands-on MATLAB solutions

Apress, 2014

MATLAB enables you to work with its graphics capabilities in almost all areas of the experimental sciences and engineering. The commands that...

Model-Based Signal Processing (Adaptive and Learning Systems for Signal Processing, Communications and Control Series)

John Wiley & Sons, 2005

A unique treatment of signal processing using a model-based perspective

Signal processing is primarily aimed at extracting useful information, while rejecting the extraneous from noisy data. If signal levels are high, then basic techniques can be applied. However, low signal levels require using the underlying physics to correct the problem...

Learn R for Applied Statistics: With Data Visualizations, Regressions, and Statistics

Apress, 2018

Gain the R programming language fundamentals for doing the applied statistics useful for data exploration and analysis in data science and data mining. This book covers topics ranging from R syntax basics, descriptive statistics, and data visualizations to inferential statistics and regressions. After learning R’s syntax, you will...

Nonparametric Tests for Censored Data

John Wiley & Sons, 2011

This book concerns testing hypotheses in non-parametric models. Generalizations of many non-parametric tests to the case of censored and truncated data are considered. Most of the test results are proved and real applications are illustrated using examples. Theories and exercises are provided. The incorrect use of many tests applying most...

Robust Statistics: Theory and Methods (Probability and Statistics)

John Wiley & Sons, 2006

Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and...

Post-Optimal Analysis in Linear Semi-Infinite Optimization (SpringerBriefs in Optimization)

Springer, 2014

Post-Optimal Analysis in Linear Semi-Infinite Optimization examines the following topics in regards to linear semi-infinite optimization: modeling uncertainty, qualitative stability analysis, quantitative stability analysis and sensitivity analysis. Linear semi-infinite optimization (LSIO) deals with linear optimization...

Constant Mean Curvature Surfaces with Boundary (Springer Monographs in Mathematics)

Springer, 2013

The study of surfaces with constant mean curvature (CMC) is one of the main topics in classical differential geometry. Moreover, CMC surfaces are important mathematical models for the physics of interfaces in the absence of gravity, where they separate two different media or for capillary phenomena. Further, as most techniques used in the...

Radar System Performance Modeling, Second Edition

Artech House Publishers, 2004

This book addresses the needs of system analysts for radar models and analysis
tools. It describes the basic principles of radar operation, how radar is configured
and used in military and civilian systems, and how to analyze and model radar at the
system level. The book presents and explains equations, computational methods,...

An Introduction to Parametric Digital Filters and Oscillators

John Wiley & Sons, 2003

Since the 1960s Digital Signal Processing (DSP) has been one of the most intensive fields of study in electronics. However, little has been produced specifically on linear non-adaptive time-variant digital filters.

The first book to be dedicated to Time-Variant Filtering

Provides a complete...

Fundamentals of Parameterized Complexity (Texts in Computer Science)

Springer, 2013

This comprehensive and self-contained textbook presents an accessible overview of the state of the art of multivariate algorithmics and complexity. Increasingly, multivariate algorithmics is having significant practical impact in many application domains, with even more developments on the horizon. The text describes how the multivariate...

Nonparametric System Identification

Cambridge University Press, 2008

Presenting a thorough overview of the theoretical foundations of non-parametric system identification for nonlinear block-oriented systems, this books shows that non-parametric regression can be successfully applied to system identification, and it highlights the achievements in doing so. With emphasis on Hammerstein, Wiener systems, and their...

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