We usually model continuous distributions with probability density functions (PDF). The probability that a value is in a certain interval is determined by integration of the PDF (see https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/probability-density-functions). The NumPy random
module has functions that represent continuous distributions—beta()
, chisquare()
, exponential()
, f()
, gamma()
, gumbel()
, laplace()
, lognormal()
, logistic()
, multivariate_normal()
, noncentral_chisquare()
, noncentral_f()
, normal()
, and others.
NumPy: Beginner's Guide
By :
NumPy: Beginner's Guide
By:
Overview of this book
Table of Contents (21 chapters)
NumPy Beginner's Guide Third Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Free Chapter
NumPy Quick Start
Beginning with NumPy Fundamentals
Getting Familiar with Commonly Used Functions
Convenience Functions for Your Convenience
Working with Matrices and ufuncs
Moving Further with NumPy Modules
Peeking into Special Routines
Assuring Quality with Testing
Plotting with matplotlib
When NumPy Is Not Enough – SciPy and Beyond
Playing with Pygame
Pop Quiz Answers
Additional Online Resources
NumPy Functions' References
Index
Customer Reviews