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• LDR

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• 001

  000001334938

• 005

  20210114163633

• 006

  m d

• 007

  cr unu||||||||

• 008

  201027s2020 enka ob 000 0 eng d

• 019

  ▼a  1176510937
  ▼a  1178652244

• 020

  ▼a  9781838988654

• 020

  ▼a  1838988653

• 020

  ▼z  9781838985097

• 035

  ▼a  2527744
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1202027150
  ▼z  (OCoLC)1176510937
  ▼z  (OCoLC)1178652244

• 037

  ▼a  CL0501000159
  ▼b  Safari Books Online

• 040

  ▼a  UMI
  ▼b  eng
  ▼e  rda
  ▼e  pn
  ▼c  UMI
  ▼d  EBLCP
  ▼d  UKAHL
  ▼d  YDX
  ▼d  N$T
  ▼d  OCLCF
  ▼d  248023

• 050

  ▼a  QA76.73.P98

• 082

  ▼a  003.3
  ▼2  23

• 100

  ▼a  Ciaburro, Giuseppe,
  ▼e  author.

• 245

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

• 260

  ▼a  Birmingham, UK:
  ▼b  Packt Publishing,
  ▼c  2020.

• 300

  ▼a  1 online resource (1 volume):
  ▼b  illustrations.

• 336

  ▼a  text
  ▼b  txt
  ▼2  rdacontent

• 337

  ▼a  computer
  ▼b  c
  ▼2  rdamedia

• 338

  ▼a  online resource
  ▼b  cr
  ▼2  rdacarrier

• 504

  ▼a  Includes bibliographical references.

• 505

  ▼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

  ▼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

  ▼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

  ▼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

  ▼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.

• 588

  ▼a  Description based on online resource; title from cover (Safari, viewed October 27, 2020).

• 590

  ▼a  OCLC control number change

• 650

  ▼a  Python (Computer program language)

• 650

  ▼a  Computer simulation.

• 650

  ▼a  Simulation methods.

• 650

  ▼a  Decision making
  ▼x  Data processing.

• 650

  ▼a  Computer programming.
  ▼2  fast
  ▼0  (OCoLC)fst00872390

• 650

  ▼a  Computer simulation.
  ▼2  fast
  ▼0  (OCoLC)fst00872518

• 650

  ▼a  Python (Computer program language)
  ▼2  fast
  ▼0  (OCoLC)fst01084736

• 655

  ▼a  Electronic books.

• 655

  ▼a  Electronic books.

• 776

  ▼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,c2020

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2527744

• 938

  ▼a  Askews and Holts Library Services
  ▼b  ASKH
  ▼n  AH37406763

• 938

  ▼a  ProQuest Ebook Central
  ▼b  EBLB
  ▼n  EBL6267443

• 938

  ▼a  YBP Library Services
  ▼b  YANK
  ▼n  301388220

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2527744

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T