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Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes
Ciaburro, Giuseppe
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| 자료유형 | E-BOOK |
|---|---|
| 서명/저자사항 | Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes/ Giuseppe Ciaburro. |
| 개인저자 | Ciaburro, Giuseppe,author. |
| 발행사항 | Birmingham, UK: Packt Publishing, 2020. |
| 형태사항 | 1 online resource (1 volume): illustrations. |
| 기타형태 저록 | Print version: Ciaburro, Giuseppe Hands-On Simulation Modeling with Python : Develop Simulation Models to Get Accurate Results and Enhance Decision-Making Processes Birmingham : Packt Publishing, Limited,c2020 |
| ISBN | 9781838988654 1838988653 |
| 서지주기 | Includes bibliographical references. |
| 내용주기 | Cover -- Title Page -- Copyright and Credits -- About Packt -- Contributors -- Table of Contents -- Preface -- Section 1: Getting Started with Numerical Simulation -- Chapter 1: Introducing Simulation Models -- 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 -- 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 -- Dynamical systems modeling -- Managing workshop machinery -- Simple harmonic oscillator -- Predator-prey model -- Summary -- Chapter 2: Understanding Randomness and Random Numbers -- Technical requirements -- Stochastic processes -- Types of stochastic process -- 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 -- The 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 The random.random() function -- The random.seed() function -- The random.uniform() function -- The random.randint() function -- The random.choice() function -- The random.sample() function -- Generating real-valued distributions -- Summary -- Chapter 3: Probability and Data Generation Processes -- 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 -- Probability density function Mean and variance -- Uniform distribution -- Binomial distribution -- Normal distribution -- Summary -- Section 2: Simulation Modeling Algorithms and Techniques -- Chapter 4: Exploring Monte Carlo Simulations -- Technical requirements -- Introducing Monte Carlo simulation -- Monte Carlo components -- First Monte Carlo application -- Monte Carlo applications -- Applying the Monte Carlo method for Pi estimation -- Understanding the central limit theorem -- Law of large numbers -- Central limit theorem -- Applying Monte Carlo simulation -- Generating probability distributions |
| 요약 | Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems. |
| 일반주제명 | Python (Computer program language) Computer simulation. Simulation methods. Decision making --Data processing. Computer programming. Computer simulation. Python (Computer program language) |
| 언어 | 영어 |
| 바로가기 | URL |
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