February 2017
Intermediate to advanced
290 pages
5h 48m
English
Content preview from Digital Signal Processing (DSP) with Python Programming
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Preface
This book addresses the fundamental bases of statistical inferences. We shall presume throughout that readers have a good working knowledge of Python® language and of the basic elements of digital signal processing.
The most recent version is Python® 3.x, but many people are still working with Python® 2.x versions. All codes provided in this book work with both these versions. The official home page of the Python® Programming Language is https://www.python.org/. Spyder® is a useful open-source integrated development environment (IDE) for programming in the Python® language. Briefly, we suggest to use the Anaconda Python distribution, which includes both Python® and Spyder®. The Anaconda Python distribution is located at https://www.continuum.io/downloads/.
The large part of the examples given in this book mainly use the modules numPy, which provides powerful numerical arrays objects, Scipy with high-level data processing routines, such as optimization, regression, interpolation and Matplotlib for plotting curves, histograms, Box and Whiskers plots, etc. See a list of useful functions p. xiii.
A brief outline of the contents of the book is given below.
Useful maths
In the first chapter, a short review of probability theory is presented, focusing on conditional probability, projection theorem and random variable transformation. A number of statistical elements will also be presented, including the great number law and the limit-central theorem.
Statistical inferences ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.