| LDR | | 05702cmm u2200613Ii 4500 |
| 001 | | 000000317895 |
| 003 | | OCoLC |
| 005 | | 20230525183635 |
| 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 248032 |
| 049 | |
▼a MAIN |
| 050 | 4 |
▼a QA76.73.P98 |
| 082 | 04 |
▼a 003.3
▼2 23 |
| 100 | 1 |
▼a Ciaburro, Giuseppe,
▼e author. |
| 245 | 10 |
▼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 | 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. |
| 588 | |
▼a Description based on online resource; title from cover (Safari, viewed October 27, 2020). |
| 590 | |
▼a OCLC control number change |
| 650 | 0 |
▼a Python (Computer program language) |
| 650 | 0 |
▼a Computer simulation. |
| 650 | 0 |
▼a Simulation methods. |
| 650 | 0 |
▼a Decision making
▼x Data processing. |
| 650 | 7 |
▼a Computer programming.
▼2 fast
▼0 (OCoLC)fst00872390 |
| 650 | 7 |
▼a Computer simulation.
▼2 fast
▼0 (OCoLC)fst00872518 |
| 650 | 7 |
▼a Python (Computer program language)
▼2 fast
▼0 (OCoLC)fst01084736 |
| 655 | 4 |
▼a Electronic books. |
| 655 | 0 |
▼a Electronic books. |
| 776 | 08 |
▼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 | 40 |
▼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 관리자 |
| 994 | |
▼a 92
▼b N$T |