Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Functional Python Programming, 3rd edition

You're reading from   Functional Python Programming, 3rd edition Use a functional approach to write succinct, expressive, and efficient Python code

Arrow left icon
Product type Paperback
Published in Dec 2022
Publisher Packt
ISBN-13 9781803232577
Length 576 pages
Edition 3rd Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Steven F. Lott Steven F. Lott
Author Profile Icon Steven F. Lott
Steven F. Lott
Arrow right icon
View More author details
Toc

Table of Contents (18) Chapters Close

Preface
1. Chapter 1: Understanding Functional Programming FREE CHAPTER 2. Chapter 2: Introducing Essential Functional Concepts 3. Chapter 3: Functions, Iterators, and Generators 4. Chapter 4: Working with Collections 5. Chapter 5: Higher-Order Functions 6. Chapter 6: Recursions and Reductions 7. Chapter 7: Complex Stateless Objects 8. Chapter 8: The Itertools Module 9. Chapter 9: Itertools for Combinatorics – Permutations and Combinations 10. Chapter 10: The Functools Module 11. Chapter 11: The Toolz Package 12. Chapter 12: Decorator Design Techniques 13. Chapter 13: The PyMonad Library 14. Chapter 14: The Multiprocessing, Threading, and Concurrent.Futures Modules 15. Chapter 15: A Functional Approach to Web Services 16. Other Books You Might Enjoy
17. Index

13.5 Implementing simulation with monads

Monads are expected to pass through a kind of pipeline: a monad will be passed as an argument to a function and a similar monad will be returned as the value of the function. The functions must be designed to accept and return similar structures.

We’ll look at a monad-based pipeline that can be used for simulation of a process. This kind of simulation is sometimes called a Monte Carlo simulation. In this case, the simulation will create a Markov chain.

A Markov chain is a model for a series of potential events. The probability of each event depends only on the state attained in the previous event. Each state of the overall system had a set of probabilities that define the events and related state changes. It fits well with games that involve random chance, like dice or cards. It also fits well with industrial processes where small random effects can ”ripple through” the system, leading to effects that may not appear to be...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image