| Autore |
Ciaburro Giuseppe
|
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa |
Birmingham : , : Packt Publishing, Limited, , 2022
|
| Descrizione fisica |
1 online resource (460 pages)
|
| Disciplina |
003.3
|
| Soggetto topico |
Python (Computer program language)
Computer simulation
Simulation methods
Decision making - Data processing
|
| ISBN |
9781523151561
1523151560
9781804614464
1804614467
|
| Formato |
Materiale a stampa  |
| Livello bibliografico |
Monografia |
| Lingua di pubblicazione |
eng
|
| Nota di contenuto |
Cover -- Title Page -- Copyright and Credits -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Getting Started with Numerical Simulation -- Chapter 1: Introducing Simulation Models -- Technical requirements -- 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 -- Exploring Discrete Event Simulation (DES) -- Finite-state machine (FSM) -- State transition table (STT) -- State transition graph (STG) -- Dynamic systems modeling -- Managing workshop machinery -- Simple harmonic oscillator -- The predator-prey model -- How to run efficient simulations to analyze real-world systems -- Summary -- Chapter 2: Understanding Randomness and Random Numbers -- Technical requirements -- Stochastic processes -- Types of stochastic processes -- 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 -- 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 -- Generating real-value distributions -- Randomness requirements for security -- Password-based authentication systems -- Random password generator -- Cryptographic random number generator -- Introducing cryptography -- Randomness and cryptography -- Encrypted/decrypted message generator -- 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 -- The probability density function -- Mean and variance -- Uniform distribution -- Binomial distribution -- Normal distribution -- Generating synthetic data -- Real data versus artificial data -- Synthetic data generation methods -- Data generation with Keras -- Data augmentation -- Simulation of power analysis -- The power of a statistical test -- Power analysis -- Summary -- Part 2: Simulation Modeling Algorithms and Techniques -- Chapter 4: Exploring Monte Carlo Simulations -- Technical requirements -- Introducing the 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 -- The central limit theorem -- Applying the Monte Carlo simulation -- Generating probability distributions -- Numerical optimization -- Project management -- Performing numerical integration using Monte Carlo -- Defining the problem -- Numerical solution -- Min-max detection -- The Monte Carlo method -- Visual representation.
Exploring sensitivity analysis concepts -- Local and global approaches -- Sensitivity analysis methods -- Sensitivity analysis in action -- Explaining the cross-entropy method -- Introducing cross-entropy -- Cross-entropy in Python -- Binary cross-entropy as a loss function -- Summary -- Chapter 5: Simulation-Based Markov Decision Processes -- Technical requirements -- Introducing agent-based models -- Overview of Markov processes -- The agent-environment interface -- Exploring MDPs -- Understanding the discounted cumulative reward -- Comparing exploration and exploitation concepts -- Introducing Markov chains -- Transition matrix -- Transition diagram -- Markov chain applications -- Introducing random walks -- One-dimensional random walk -- Simulating a 1D random walk -- Simulating a weather forecast -- Bellman equation explained -- Dynamic programming concepts -- Principle of optimality -- Bellman equation -- Multi-agent simulation -- Schelling's model of segregation -- Python Schelling model -- Summary -- Chapter 6: Resampling Methods -- Technical requirements -- Introducing resampling methods -- Sampling concepts overview -- Reasoning about sampling -- Pros and cons of sampling -- Probability sampling -- How sampling works -- Exploring the Jackknife technique -- Defining the Jackknife method -- Estimating the coefficient of variation -- Applying Jackknife resampling using Python -- Demystifying bootstrapping -- Introducing bootstrapping -- Bootstrap definition problem -- Bootstrap resampling using Python -- Comparing Jackknife and bootstrap -- Applying bootstrapping regression -- Explaining permutation tests -- Performing a permutation test -- Approaching cross-validation techniques -- Validation set approach -- Leave-one-out cross-validation -- k-fold cross-validation -- Cross-validation using Python -- Summary.
Chapter 7: Using Simulation to Improve and Optimize Systems -- Technical requirements -- Introducing numerical optimization techniques -- Defining an optimization problem -- Explaining local optimality -- Exploring the gradient descent technique -- Defining descent methods -- Approaching the gradient descent algorithm -- Understanding the learning rate -- Explaining the trial and error method -- Implementing gradient descent in Python -- Understanding the Newton-Raphson method -- Using the Newton-Raphson algorithm for root finding -- Approaching Newton-Raphson for numerical optimization -- Applying the Newton-Raphson technique -- The secant method -- Deepening our knowledge of stochastic gradient descent -- Approaching the EM algorithm -- EM algorithm for Gaussian mixture -- Understanding Simulated Annealing (SA) -- Iterative improvement algorithms -- SA in action -- Discovering multivariate optimization methods in Python -- The Nelder-Mead method -- Powell's conjugate direction algorithm -- Summarizing other optimization methodologies -- Summary -- Chapter 8: Introducing Evolutionary Systems -- Technical requirements -- Introducing SC -- Fuzzy logic (FL) -- Artificial neural network (ANN) -- Evolutionary computation -- Understanding genetic programming -- Introducing the genetic algorithm (GA) -- The basics of GA -- Genetic operators -- Applying a GA for search and optimization -- Performing symbolic regression (SR) -- Exploring the CA model -- Game-of-life -- Wolfram code for CA -- Summary -- Part 3: Simulation Applications to Solve Real-World Problems -- Chapter 9: Using Simulation Models for Financial Engineering -- Technical requirements -- Understanding the geometric Brownian motion model -- Defining a standard Brownian motion -- Addressing the Wiener process as random walk -- Implementing a standard Brownian motion.
Using Monte Carlo methods for stock price prediction -- Exploring the Amazon stock price trend -- Handling the stock price trend as a time series -- Introducing the Black-Scholes model -- Applying the Monte Carlo simulation -- Studying risk models for portfolio management -- Using variance as a risk measure -- Introducing the Value-at-Risk metric -- Estimating VaR for some NASDAQ assets -- Summary -- Chapter 10: Simulating Physical Phenomena Using Neural Networks -- Technical requirements -- Introducing the basics of neural networks -- Understanding biological neural networks -- Exploring ANNs -- Understanding feedforward neural networks -- Exploring neural network training -- Simulating airfoil self-noise using ANNs -- Importing data using pandas -- Scaling the data using sklearn -- Viewing the data using Matplotlib -- Splitting the data -- Explaining multiple linear regression -- Understanding a multilayer perceptron regressor model -- Approaching deep neural networks -- Getting familiar with convolutional neural networks -- Examining recurrent neural networks -- Analyzing long short-term memory networks -- Exploring GNNs -- Introducing graph theory -- Adjacency matrix -- GNNs -- Simulation modeling using neural network techniques -- Concrete quality prediction model -- Summary -- Chapter 11: Modeling and Simulation for Project Management -- Technical requirements -- Introducing project management -- Understanding what-if analysis -- Managing a tiny forest problem -- Summarizing the Markov decision process -- Exploring the optimization process -- Introducing MDPtoolbox -- Defining the tiny forest management example -- Addressing management problems using MDPtoolbox -- Changing the probability of a fire starting -- Scheduling project time using the Monte Carlo simulation -- Defining the scheduling grid -- Estimating the task's time.
Developing an algorithm for project scheduling.
|
| Record Nr. | UNINA-9911006511603321 |
Info
Hands-On Machine Learning on Google Cloud Platform [[electronic resource] /] / Ciaburro, Giuseppe
