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Simulation with Python: Develop Simulation and Modeling in Natural Sciences, Engineering, and Social Sciences 1st ed. [Pehme köide]

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  • Formaat: Paperback / softback, 166 pages, kõrgus x laius: 254x178 mm, kaal: 361 g, 80 Illustrations, color; 10 Illustrations, black and white; XV, 166 p. 90 illus., 80 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 24-Aug-2022
  • Kirjastus: APress
  • ISBN-10: 1484281845
  • ISBN-13: 9781484281840
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Simulation with Python: Develop Simulation and Modeling in Natural Sciences, Engineering, and Social Sciences 1st ed.Suurem pilt
  • Formaat: Paperback / softback, 166 pages, kõrgus x laius: 254x178 mm, kaal: 361 g, 80 Illustrations, color; 10 Illustrations, black and white; XV, 166 p. 90 illus., 80 illus. in color., 1 Paperback / softback
  • Ilmumisaeg: 24-Aug-2022
  • Kirjastus: APress
  • ISBN-10: 1484281845
  • ISBN-13: 9781484281840
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781484281840.html
Märksõnad:
Intermediate-Advanced user level

Understand the theory and implementation of simulation. This book covers simulation topics from a scenario-driven approach using Python and rich visualizations and tabulations. 

The book discusses simulation used in the natural and social sciences and with simulations taken from the top algorithms used in the industry today. The authors use an engaging approach that mixes mathematics and programming experiments with beginning-intermediate level Python code to create an immersive learning experience that is cohesive and integrated. 

After reading this book, you will have an understanding of simulation used in natural sciences, engineering, and social sciences using Python.


What You'll Learn
  • Use Python and numerical computation to demonstrate the power of simulation
  • Choose a paradigm to run a simulation
  • Draw statistical insights from numerical experiments
  • Know how simulation is used to solve real-world problems 

Who This Book Is For

Entry-level to mid-level Python developers from various backgrounds, including backend developers, academic research programmers, data scientists, and machine learning engineers. The book is also useful to high school students and college undergraduates and graduates with STEM backgrounds.

About the Authors ix
About the Technical Reviewer xi
Acknowledgments xiii
Introduction xv
Chapter 1 Calculating Pi with Monte Carlo Simulation
1(18)
Background
1(1)
The Wise Persons' Competition
1(1)
Estimating Pi by Sprinkling Grains
2(8)
Exercise
10(1)
Contain the Goat!
10(1)
What Randomness?
11(7)
Exercise
18(1)
Summary
18(1)
Chapter 2 Markov Chain, a Peek into the Future
19(20)
Weather Forecasting
19(6)
Eigenstates of Markov Chains
25(2)
Exercise
27(1)
Markov Chain Applications
27(1)
A Random Walk That Has an End
28(3)
Sonnet Written by Drunk Shakespeare
31(5)
Exercise
36(1)
Summary
37(2)
Chapter 3 Multi-armed Bandits, Probability Simulation, and Bayesian Statistics
39(16)
Random Pick and Naive Greedy Approach
40(4)
Greedy-Epsilon: Greedy but Not Always
44(1)
An Improved Greedy-Epsilon Algorithm
45(1)
Exercise
46(1)
The Bayesian Way, a Primer on Bayesian Statistics
47(6)
Exercise
53(1)
Summary
54(1)
Chapter 4 Balls in a 2-D Box, a Simple Physics Engine
55(22)
One Ball in a 2-D Box
55(2)
Physics Law of Motion
57(3)
Collision Detection
60(5)
Exercise
65(1)
Multiple Balls in a 2-D Box
65(1)
Update of Positions and Velocity upon Collision
65(9)
Collision Detection in Multiple-Ball Scenario
74(1)
Exercise
75(1)
Summary
76(1)
Chapter 5 Percolation, Threshold, and Phase Change
77(18)
Problem Introduction
78(4)
Percolation and the Critical Probability
82(1)
An Analytical Solution for the 1-D Case
82(1)
A Simulation for the 2-D Case
83(7)
Exercise
90(1)
Another Interesting Statistic in 2-D Grid Percolation
90(3)
Exercise
93(1)
Summary
94(1)
Chapter 6 Queuing System: How Stock Trades Are Made
95(16)
Trading Process Fundamentals
95(1)
The Order Book
96(2)
Create the Interfaces and Determine the Data Schema
98(4)
Implement Order Book Logic
102(5)
Hook the Bots and Engine Together
107(2)
Exercises and Extension Ideas
109(1)
Multiple Bots
109(1)
An Informed Bot
110(1)
Order Book Visualization
110(1)
Order Cancellation Support
110(1)
Stop Orders Support
110(1)
Summary
110(1)
Chapter 7 Rock, Scissors, and Paper: Multi-agent Simulation
111(16)
Community Formation on a Street
112(4)
Exercise
116(1)
How to Win a Global Rock, Paper, and Scissors Contest
116(9)
Exercise
125(1)
Summary
126(1)
Chapter 8 Disease Spreading, Simulating C0VID-19 Outbreak
127(12)
Simplifying the Real World
127(2)
The SI Model
129(4)
Exercise
133(1)
The SIR Model
133(4)
Exercise
137(1)
Summary
137(2)
Chapter 9 Misinformation Spreading and Simulations on a Graph
139(24)
Model the Social Network
139(4)
Simulate Misinformation Spreading
143(1)
Simple Cases
144(8)
Misinformation Spreading on Different Networks
152(9)
Exercise
161(1)
Summary
161(2)
Index 163
Ron Li is a long-term and enthusiastic educator. He has been a researcher, data science instructor, and business intelligence engineer. Ron published a highly rated (4.5-star rating out of 5 on amazon) book titled Essential Statistics for Non-STEM Data Analysts. He has also authored/co-authored academic papers, taught (pro bono) data science to non-STEM professionals, and gives talks at conferences such as PyData.  Aiichiro Nakano is a Professor of Computer Science with joint appointments in Physics & Astronomy, Chemical Engineering & Materials Science, Biological Sciences, and at the Collaboratory for Advanced Computing and Simulations at the University of Southern California. He received a PhD in physics from the University of Tokyo, Japan, in 1989. He has authored more than 360 refereed articles in the areas of scalable scientific algorithms, massive data visualization and analysis, and computational materials science.