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...
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham, UK :
Packt Publishing,
2020.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | 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. |
| Beschreibung: | 1 online resource (1 volume) : illustrations |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9781838988654 1838988653 |
Internformat
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| 100 | 1 | |a Ciaburro, Giuseppe, |e author. |0 http://id.loc.gov/authorities/names/no2017148956 | |
| 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 | ||
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| 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. | ||
| 650 | 0 | |a Python (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh96008834 | |
| 650 | 0 | |a Computer simulation. |0 http://id.loc.gov/authorities/subjects/sh85029533 | |
| 650 | 0 | |a Simulation methods. |0 http://id.loc.gov/authorities/subjects/sh85122767 | |
| 650 | 0 | |a Decision making |x Data processing. | |
| 650 | 2 | |a Computer Simulation |0 https://id.nlm.nih.gov/mesh/D003198 | |
| 650 | 6 | |a Python (Langage de programmation) | |
| 650 | 6 | |a Simulation par ordinateur. | |
| 650 | 6 | |a Méthodes de simulation. | |
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| 650 | 7 | |a Computer simulation |2 fast | |
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| 776 | 0 | 8 | |i Print version: |a Ciaburro, Giuseppe. |t Hands-On Simulation Modeling with Python : Develop Simulation Models to Get Accurate Results and Enhance Decision-Making Processes. |d Birmingham : Packt Publishing, Limited, ©2020 |
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| adam_text | |
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| author | Ciaburro, Giuseppe |
| author_GND | http://id.loc.gov/authorities/names/no2017148956 |
| author_facet | Ciaburro, Giuseppe |
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| contents | 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 |
| ctrlnum | (OCoLC)1202027150 |
| dewey-full | 003.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 003 - Systems |
| dewey-raw | 003.3 |
| dewey-search | 003.3 |
| dewey-sort | 13.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Electronic eBook |
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| spelling | Ciaburro, Giuseppe, author. http://id.loc.gov/authorities/names/no2017148956 Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / Giuseppe Ciaburro. Birmingham, UK : Packt Publishing, 2020. 1 online resource (1 volume) : illustrations text txt rdacontent computer c rdamedia online resource cr rdacarrier Online resource; title from cover (Safari, viewed October 27, 2020). Includes bibliographical references. 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 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. Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Computer simulation. http://id.loc.gov/authorities/subjects/sh85029533 Simulation methods. http://id.loc.gov/authorities/subjects/sh85122767 Decision making Data processing. Computer Simulation https://id.nlm.nih.gov/mesh/D003198 Python (Langage de programmation) Simulation par ordinateur. Méthodes de simulation. Prise de décision Informatique. simulation. aat simulation methods. aat Simulation methods fast Decision making Data processing fast Computer programming fast Computer simulation fast Python (Computer program language) fast has work: Hands-on simulation modeling with Python (Text) https://id.oclc.org/worldcat/entity/E39PCG3c8qqrVXYkvG7CWk87Xm https://id.oclc.org/worldcat/ontology/hasWork 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, ©2020 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2527744 Volltext |
| spellingShingle | Ciaburro, Giuseppe Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / 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 Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Computer simulation. http://id.loc.gov/authorities/subjects/sh85029533 Simulation methods. http://id.loc.gov/authorities/subjects/sh85122767 Decision making Data processing. Computer Simulation https://id.nlm.nih.gov/mesh/D003198 Python (Langage de programmation) Simulation par ordinateur. Méthodes de simulation. Prise de décision Informatique. simulation. aat simulation methods. aat Simulation methods fast Decision making Data processing fast Computer programming fast Computer simulation fast Python (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh96008834 http://id.loc.gov/authorities/subjects/sh85029533 http://id.loc.gov/authorities/subjects/sh85122767 https://id.nlm.nih.gov/mesh/D003198 |
| title | Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / |
| title_auth | Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / |
| title_exact_search | Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / |
| title_full | Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / Giuseppe Ciaburro. |
| title_fullStr | Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / Giuseppe Ciaburro. |
| title_full_unstemmed | Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes / Giuseppe Ciaburro. |
| title_short | Hands-on simulation modeling with Python : |
| title_sort | hands on simulation modeling with python develop simulation models to get accurate results and enhance decision making processes |
| title_sub | develop simulation models to get accurate results and enhance decision-making processes / |
| topic | Python (Computer program language) http://id.loc.gov/authorities/subjects/sh96008834 Computer simulation. http://id.loc.gov/authorities/subjects/sh85029533 Simulation methods. http://id.loc.gov/authorities/subjects/sh85122767 Decision making Data processing. Computer Simulation https://id.nlm.nih.gov/mesh/D003198 Python (Langage de programmation) Simulation par ordinateur. Méthodes de simulation. Prise de décision Informatique. simulation. aat simulation methods. aat Simulation methods fast Decision making Data processing fast Computer programming fast Computer simulation fast Python (Computer program language) fast |
| topic_facet | Python (Computer program language) Computer simulation. Simulation methods. Decision making Data processing. Computer Simulation Python (Langage de programmation) Simulation par ordinateur. Méthodes de simulation. Prise de décision Informatique. simulation. simulation methods. Simulation methods Decision making Data processing Computer programming Computer simulation |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2527744 |
| work_keys_str_mv | AT ciaburrogiuseppe handsonsimulationmodelingwithpythondevelopsimulationmodelstogetaccurateresultsandenhancedecisionmakingprocesses |