Book Image

Applying Math with Python - Second Edition

By : Sam Morley
Book Image

Applying Math with Python - Second Edition

By: Sam Morley

Overview of this book

The updated edition of Applying Math with Python will help you solve complex problems in a wide variety of mathematical fields in simple and efficient ways. Old recipes have been revised for new libraries and several recipes have been added to demonstrate new tools such as JAX. You'll start by refreshing your knowledge of several core mathematical fields and learn about packages covered in Python's scientific stack, including NumPy, SciPy, and Matplotlib. As you progress, you'll gradually get to grips with more advanced topics of calculus, probability, and networks (graph theory). Once you’ve developed a solid base in these topics, you’ll have the confidence to set out on math adventures with Python as you explore Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Table of Contents (13 chapters)

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...