Mathematics of Economics and Business Today, a firm understanding of mathematics is essential for any serious student of economics. Students of economics need nowadays several important mathematical tools. These include calculus for functions of one or several variables as well as a basic understanding of optimization with and without constraints, e.g. linear programming plays an... Nonlinear Digital Filtering with Python: An Introduction
Nonlinear Digital Filtering with Python: An Introduction discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Adopting both structural and behavioral approaches in characterizing...
Guide to Computational Geometry Processing: Foundations, Algorithms, and Methods
This book reviews the algorithms for processing geometric data, with a practical focus on important techniques not covered by traditional courses on computer vision and computer graphics. Features: presents an overview of the underlying mathematical theory, covering vector spaces, metric space, affine spaces, differential geometry, and finite...
The Linear Algebra a Beginning Graduate Student Ought to Know
Linear algebra is a living, active branch of mathematics which is central to almost all other areas of mathematics, both pure and applied, as well as to computer science, to the physical, biological, and social sciences, and to engineering. It encompasses an extensive corpus of theoretical results as well as a large and rapidly-growing body...
Schaum's Outline of Linear Algebra
This third edition of the successful outline in linear algebra--which sold more than 400,000 copies in its past two editions--has been thoroughly updated to increase its applicability to the fields in which linear algebra is now essential: computer science, engineering, mathematics, physics, and quantitative analysis. Revised coverage...
Learning Bayesian Models with R
Become an expert in Bayesian Machine Learning methods using R and apply them to solve real-world big data problems
About This Book
Understand the principles of Bayesian Inference with less mathematical equations
Learn state-of-the art Machine Learning methods
|Result Page: 53 52 51 50 49 48 47 46 45 44 |