Hands-on simulation modeling with Python: develop simulation models to get accurate results and enhance decision-making processes/ Giuseppe Ciaburro.

Ciaburro  Giuseppe

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dc.contributor.author	Ciaburro Giuseppe
dc.date.accessioned	2024-01-29T21:46:14Z
dc.date.available	2024-01-29T21:46:14Z
dc.date.issued	2020
dc.identifier.citation	Ciaburro. Hands-on simulation modeling with Python: develop simulation models to get accurate results and enhance decision-making processes - 1 online resource (1 volume) : - URL: https://libweb.kpfu.ru/ebsco/pdf/2527744.pdf
dc.identifier.isbn	9781838988654
dc.identifier.isbn	1838988653
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/179880
dc.description	Includes bibliographical references.
dc.description.abstract	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.
dc.description.tableofcontents	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
dc.description.tableofcontents	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
dc.description.tableofcontents	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
dc.description.tableofcontents	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
dc.description.tableofcontents	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
dc.language	English
dc.language.iso	en
dc.subject.other	Python (Computer program language)
dc.subject.other	Computer simulation.
dc.subject.other	Simulation methods.
dc.subject.other	Decision making -- Data processing.
dc.subject.other	Computer programming.
dc.subject.other	Computer simulation.
dc.subject.other	Python (Computer program language)
dc.subject.other	Electronic books.
dc.subject.other	Electronic books.
dc.title	Hands-on simulation modeling with Python: develop simulation models to get accurate results and enhance decision-making processes/ Giuseppe Ciaburro.
dc.type	Book
dc.description.pages	1 online resource (1 volume) :
dc.collection	Электронно-библиотечные системы
dc.source.id	EN05CEBSCO05C595