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Hands-On Simulation Modeling with Python Develop Simulation Models for Improved Efficiency and Precision in the Decision-Making Process /
Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key Features Understand various statistical and physical simulations to improve systems using Python Learn to...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2022.
|
| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
| LEADER | 00000cam a22000007a 4500 | ||
|---|---|---|---|
| 001 | OR_on1352969684 | ||
| 003 | OCoLC | ||
| 005 | 20231017213018.0 | ||
| 006 | m o d | ||
| 007 | cr cnu---unuuu | ||
| 008 | 221203s2022 enk o 000 0 eng d | ||
| 040 | |a EBLCP |b eng |c EBLCP |d ORMDA |d OCLCF |d OCLCO | ||
| 020 | |a 9781804614464 | ||
| 020 | |a 1804614467 | ||
| 029 | 1 | |a AU@ |b 000073244381 | |
| 035 | |a (OCoLC)1352969684 | ||
| 037 | |a 9781804616888 |b O'Reilly Media | ||
| 050 | 4 | |a QA76.73.P98 | |
| 082 | 0 | 4 | |a 003.3 |2 23/eng/20221213 |
| 049 | |a UAMI | ||
| 100 | 1 | |a Ciaburro, Giuseppe, |e author. | |
| 245 | 1 | 0 | |a Hands-On Simulation Modeling with Python |h [electronic resource] : |b Develop Simulation Models for Improved Efficiency and Precision in the Decision-Making Process / |c Giuseppe Ciaburro. |
| 250 | |a 2nd ed. | ||
| 260 | |a Birmingham : |b Packt Publishing, Limited, |c 2022. | ||
| 300 | |a 1 online resource (460 p.) | ||
| 500 | |a Description based upon print version of record. | ||
| 505 | 0 | |a Cover -- Title Page -- Copyright and Credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with Numerical Simulation -- Chapter 1: Introducing Simulation Models -- Technical requirements -- Introducing simulation models -- Decision-making workflow -- Comparing modeling and simulation -- Pros and cons of simulation modeling -- Simulation modeling terminology -- Classifying simulation models -- Comparing static and dynamic models -- Comparing deterministic and stochastic models -- Comparing continuous and discrete models | |
| 505 | 8 | |a Approaching a simulation-based problem -- Problem analysis -- Data collection -- Setting up the simulation model -- Simulation software selection -- Verification of the software solution -- Validation of the simulation model -- Simulation and analysis of results -- Exploring Discrete Event Simulation (DES) -- Finite-state machine (FSM) -- State transition table (STT) -- State transition graph (STG) -- Dynamic systems modeling -- Managing workshop machinery -- Simple harmonic oscillator -- The predator-prey model -- How to run efficient simulations to analyze real-world systems -- Summary | |
| 505 | 8 | |a Chapter 2: Understanding Randomness and Random Numbers -- Technical requirements -- Stochastic processes -- Types of stochastic processes -- Examples of stochastic processes -- The Bernoulli process -- Random walk -- The Poisson process -- Random number simulation -- Probability distribution -- Properties of random numbers -- The pseudorandom number generator -- The pros and cons of a random number generator -- Random number generation algorithms -- Linear congruential generator -- Random numbers with uniform distribution -- Lagged Fibonacci generator -- Testing uniform distribution | |
| 505 | 8 | |a Chi-squared test -- Uniformity test -- Exploring generic methods for random distributions -- The inverse transform sampling method -- The acceptance-rejection method -- Random number generation using Python -- Introducing the random module -- Generating real-value distributions -- Randomness requirements for security -- Password-based authentication systems -- Random password generator -- Cryptographic random number generator -- Introducing cryptography -- Randomness and cryptography -- Encrypted/decrypted message generator -- Summary -- Chapter 3: Probability and Data Generation Processes | |
| 505 | 8 | |a Technical requirements -- Explaining probability concepts -- Types of events -- Calculating probability -- Probability definition with an example -- Understanding Bayes' theorem -- Compound probability -- Bayes' theorem -- Exploring probability distributions -- The probability density function -- Mean and variance -- Uniform distribution -- Binomial distribution -- Normal distribution -- Generating synthetic data -- Real data versus artificial data -- Synthetic data generation methods -- Data generation with Keras -- Data augmentation -- Simulation of power analysis | |
| 500 | |a The power of a statistical test | ||
| 520 | |a Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key Features Understand various statistical and physical simulations to improve systems using Python Learn to create the numerical prototype of a real model using hands-on examples Evaluate performance and output results based on how the prototype would work in the real world Book Description Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that'll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you'll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you'll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learn Get to grips with the concept of randomness and the data generation process Delve into resampling methods Discover how to work with Monte Carlo simulations Utilize simulations to improve or optimize systems Find out how to run efficient simulations to analyze real-world systems Understand how to simulate random walks using Markov chains Who this book is for This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python. | ||
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Python (Computer program language) | |
| 650 | 0 | |a Computer simulation. | |
| 650 | 0 | |a Simulation methods. | |
| 650 | 0 | |a Decision making |x Data processing. | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Simulation par ordinateur. | |
| 650 | 6 | |a Méthodes de simulation. | |
| 650 | 6 | |a Prise de décision |x Informatique. | |
| 650 | 7 | |a simulation. |2 aat | |
| 650 | 7 | |a simulation methods. |2 aat | |
| 650 | 7 | |a Computer simulation |2 fast | |
| 650 | 7 | |a Decision making |x Data processing |2 fast | |
| 650 | 7 | |a Python (Computer program language) |2 fast | |
| 650 | 7 | |a Simulation methods |2 fast | |
| 776 | 0 | 8 | |i Print version: |a Ciaburro, Giuseppe |t Hands-On Simulation Modeling with Python |d Birmingham : Packt Publishing, Limited,c2022 |
| 856 | 4 | 0 | |u https://learning.oreilly.com/library/view/~/9781804616888/?ar |z Texto completo (Requiere registro previo con correo institucional) |
| 938 | |a ProQuest Ebook Central |b EBLB |n EBL30279149 | ||
| 994 | |a 92 |b IZTAP | ||