Hands-on simulation modeling with Python develop simulation models to get accurate results and enhance decision-making processes
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 methodologi...
| Main Author: | |
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| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK
Packt Publishing,
2020.
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| Subjects: | |
| Online Access: | EBSCOhost Перейти в каталог НБ ТГУ |
| LEADER | 05209cam a2200541Ii 4500 | ||
|---|---|---|---|
| 001 | koha001014416 | ||
| 003 | OCoLC | ||
| 005 | 20250222070034.0 | ||
| 006 | m d | ||
| 007 | cr unu|||||||| | ||
| 008 | 201027s2020 enka ob 000 0 eng d | ||
| 035 | |a koha001014416 | ||
| 040 | |a UMI |b eng |e rda |e pn |c UMI |d EBLCP |d UKAHL |d YDX |d N$T |d OCLCF | ||
| 019 | |a 1176510937 |a 1178652244 | ||
| 020 | |a 9781838988654 | ||
| 020 | |a 1838988653 | ||
| 020 | |z 9781838985097 | ||
| 037 | |a CL0501000159 |b Safari Books Online | ||
| 050 | 4 | |a QA76.73.P98 | |
| 082 | 0 | 4 | |a 003.3 |2 23 |
| 049 | |a MAIN | ||
| 100 | 1 | |a Ciaburro, Giuseppe, |9 913675 | |
| 245 | 1 | 0 | |a Hands-on simulation modeling with Python |b develop simulation models to get accurate results and enhance decision-making processes |c Giuseppe Ciaburro. |
| 264 | 1 | |a Birmingham, UK |b Packt Publishing, |c 2020. | |
| 300 | |a 1 online resource (1 volume) |b illustrations | ||
| 588 | |a Description based on online resource; title from cover (Safari, viewed October 27, 2020). | ||
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 520 | |a 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. | ||
| 653 | 0 | |a Python (Computer program language) | |
| 653 | 0 | |a Computer simulation. | |
| 653 | 0 | |a Simulation methods. | |
| 653 | 0 | |a Decision making |x Data processing. | |
| 653 | 7 | |a Computer programming. |2 fast |0 (OCoLC)fst00872390 | |
| 653 | 7 | |a Computer simulation. |2 fast |0 (OCoLC)fst00872518 | |
| 653 | 7 | |a Python (Computer program language) |2 fast |0 (OCoLC)fst01084736 | |
| 655 | 0 | |a EBSCO eBooks |9 905790 | |
| 655 | 4 | |a Electronic books. |9 899821 | |
| 655 | 0 | |a Electronic books. |9 899821 | |
| 856 | 4 | 0 | |3 EBSCOhost |u https://www.lib.tsu.ru/limit/2023/EBSCO/2527744.pdf |
| 856 | |y Перейти в каталог НБ ТГУ |u https://koha.lib.tsu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=1014416 | ||
| 910 | |a EBSCO eBooks | ||
| 999 | |c 1014416 |d 1014416 | ||
| 039 | |||