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Applying Math with Python

You're reading from   Applying Math with Python Over 70 practical recipes for solving real-world computational math problems

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Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781804618370
Length 376 pages
Edition 2nd Edition
Languages
Concepts
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Author (1):
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Sam Morley Sam Morley
Author Profile Icon Sam Morley
Sam Morley
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Table of Contents (13) Chapters Close

Preface 1. Chapter 1: An Introduction to Basic Packages, Functions, and Concepts 2. Chapter 2: Mathematical Plotting with Matplotlib FREE CHAPTER 3. Chapter 3: Calculus and Differential Equations 4. Chapter 4: Working with Randomness and Probability 5. Chapter 5: Working with Trees and Networks 6. Chapter 6: Working with Data and Statistics 7. Chapter 7: Using Regression and Forecasting 8. Chapter 8: Geometric Problems 9. Chapter 9: Finding Optimal Solutions 10. Chapter 10: Improving Your Productivity 11. Index 12. Other Books You May Enjoy

Working with random processes

In this recipe, we will examine a simple example of a random process that models the number of bus arrivals at a stop over time. This process is called a Poisson process. A Poisson process, , has a single parameter, , which is usually called the intensity or rate, and the probability that takes the value at a given time is given by the following formula:

This equation describes the probability that buses have arrived by time . Mathematically, this equation means that has a Poisson distribution with the parameter . There is, however, an easy way to construct a Poisson process by taking sums of inter-arrival times that follow an exponential distribution. For instance, let be the time between the ()-st arrival and the -th arrival, which are exponentially distributed with parameter . Now, we take the following equation:

Here, the number is the maximum such that . This is the construction that we will work through in this recipe. We will...

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