Hands On Simulation Modeling With Python

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Hands-On Simulation Modeling with Python

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1838988653
Total Pages : 347 pages
Book Rating : 4.8/5 (389 download)

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Book Synopsis Hands-On Simulation Modeling with Python by : Giuseppe Ciaburro

Download or read book Hands-On Simulation Modeling with Python written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2020-07-17 with total page 347 pages. Available in PDF, EPUB and Kindle. Book excerpt: Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key Features Learn to create a digital prototype of a real model using hands-on examples Evaluate the performance and output of your prototype using simulation modeling techniques Understand various statistical and physical simulations to improve systems using Python Book Description Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learn Gain an overview of the different types of simulation models Get to grips with the concepts of randomness and data generation process Understand how to work with discrete and continuous distributions Work with Monte Carlo simulations to calculate a definite integral Find out how to simulate random walks using Markov chains Obtain robust estimates of confidence intervals and standard errors of population parameters Discover how to use optimization methods in real-life applications Run efficient simulations to analyze real-world systems Who this book is for Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.

Modeling and Simulation in Python

Author : Allen B. Downey
Publisher : No Starch Press
ISBN 13 : 1718502176
Total Pages : 277 pages
Book Rating : 4.7/5 (185 download)

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Book Synopsis Modeling and Simulation in Python by : Allen B. Downey

Download or read book Modeling and Simulation in Python written by Allen B. Downey and published by No Starch Press. This book was released on 2023-05-30 with total page 277 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling and Simulation in Python teaches readers how to analyze real-world scenarios using the Python programming language, requiring no more than a background in high school math. Modeling and Simulation in Python is a thorough but easy-to-follow introduction to physical modeling—that is, the art of describing and simulating real-world systems. Readers are guided through modeling things like world population growth, infectious disease, bungee jumping, baseball flight trajectories, celestial mechanics, and more while simultaneously developing a strong understanding of fundamental programming concepts like loops, vectors, and functions. Clear and concise, with a focus on learning by doing, the author spares the reader abstract, theoretical complexities and gets right to hands-on examples that show how to produce useful models and simulations.

A Student's Guide to Python for Physical Modeling

Author : Jesse M. Kinder
Publisher : Princeton University Press
ISBN 13 : 0691223661
Total Pages : 241 pages
Book Rating : 4.6/5 (912 download)

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Book Synopsis A Student's Guide to Python for Physical Modeling by : Jesse M. Kinder

Download or read book A Student's Guide to Python for Physical Modeling written by Jesse M. Kinder and published by Princeton University Press. This book was released on 2021-08-03 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: A fully updated tutorial on the basics of the Python programming language for science students Python is a computer programming language that has gained popularity throughout the sciences. This fully updated second edition of A Student's Guide to Python for Physical Modeling aims to help you, the student, teach yourself enough of the Python programming language to get started with physical modeling. You will learn how to install an open-source Python programming environment and use it to accomplish many common scientific computing tasks: importing, exporting, and visualizing data; numerical analysis; and simulation. No prior programming experience is assumed. This guide introduces a wide range of useful tools, including: Basic Python programming and scripting Numerical arrays Two- and three-dimensional graphics Animation Monte Carlo simulations Numerical methods, including solving ordinary differential equations Image processing Numerous code samples and exercises—with solutions—illustrate new ideas as they are introduced. This guide also includes supplemental online resources: code samples, data sets, tutorials, and more. This edition includes new material on symbolic calculations with SymPy, an introduction to Python libraries for data science and machine learning (pandas and sklearn), and a primer on Python classes and object-oriented programming. A new appendix also introduces command line tools and version control with Git.

Computer Simulation

Author : Yahya Esmail Osais
Publisher : CRC Press
ISBN 13 : 1498726836
Total Pages : 314 pages
Book Rating : 4.4/5 (987 download)

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Book Synopsis Computer Simulation by : Yahya Esmail Osais

Download or read book Computer Simulation written by Yahya Esmail Osais and published by CRC Press. This book was released on 2017-11-28 with total page 314 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computer simulation is an effective and popular universal tool that can be applied to almost all disciplines. Requiring only basic knowledge of programming, mathematics, and probability theory, Computer Simulation: A Foundational Approach Using Python takes a hands-on approach to programming to introduce the fundamentals of computer simulation. The main target of the book is computer science and engineering students who are interested mainly in directly applying the techniques to their research problems. The book will be of great interest to senior undergraduate and starting graduate students in the fields of computer science and engineering and industrial engineering.

Hands-On Simulation Modeling with Python

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
ISBN 13 : 1804614467
Total Pages : 460 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Hands-On Simulation Modeling with Python by : Giuseppe Ciaburro

Download or read book Hands-On Simulation Modeling with Python written by Giuseppe Ciaburro and published by Packt Publishing Ltd. This book was released on 2022-11-30 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key FeaturesUnderstand various statistical and physical simulations to improve systems using PythonLearn to create the numerical prototype of a real model using hands-on examplesEvaluate performance and output results based on how the prototype would work in the real worldBook Description Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that'll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you'll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you'll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges. What you will learnGet to grips with the concept of randomness and the data generation processDelve into resampling methodsDiscover how to work with Monte Carlo simulationsUtilize simulations to improve or optimize systemsFind out how to run efficient simulations to analyze real-world systemsUnderstand how to simulate random walks using Markov chainsWho this book is for This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.

Hands-On Python for Finance

Author : Krish Naik
Publisher :
ISBN 13 : 9781789346374
Total Pages : 378 pages
Book Rating : 4.3/5 (463 download)

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Book Synopsis Hands-On Python for Finance by : Krish Naik

Download or read book Hands-On Python for Finance written by Krish Naik and published by . This book was released on 2019-03-29 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Key Features Understand Python data structure fundamentals and work with time series data Use popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative finance Explore various Python programs and learn finance paradigms Book Description Python is one of the most popular languages used for quantitative finance. With this book, you'll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management. The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you'll implement time series analysis using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you'll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models that will show you how to determine the price of a call option by analyzing price variation. You'll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction. By the end of this book, you will be equipped with the skills you need to perform different financial analysis tasks using Python What you will learn Clean financial data with data preprocessing Visualize financial data using histograms, color plots, and graphs Perform time series analysis with pandas for forecasting Estimate covariance and the correlation between securities and stocks Optimize your portfolio to understand risks when there is a possibility of higher returns Calculate expected returns of a stock to measure the performance of a portfolio manager Create a prediction model using recurrent neural networks (RNN) with Keras and TensorFlow Who this book is for This book is ideal for aspiring data scientists, Python developers and anyone who wants to start performing quantitative finance using Python. You can also make this beginner-level guide your first choice if you're looking to pursue a career as a financial analyst or a data analyst. Working knowledge of Python programming language is necessary.

Hands-On Data Science and Python Machine Learning

Author : Frank Kane
Publisher : Packt Publishing Ltd
ISBN 13 : 1787280225
Total Pages : 415 pages
Book Rating : 4.7/5 (872 download)

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Book Synopsis Hands-On Data Science and Python Machine Learning by : Frank Kane

Download or read book Hands-On Data Science and Python Machine Learning written by Frank Kane and published by Packt Publishing Ltd. This book was released on 2017-07-31 with total page 415 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers the fundamentals of machine learning with Python in a concise and dynamic manner. It covers data mining and large-scale machine learning using Apache Spark. About This Book Take your first steps in the world of data science by understanding the tools and techniques of data analysis Train efficient Machine Learning models in Python using the supervised and unsupervised learning methods Learn how to use Apache Spark for processing Big Data efficiently Who This Book Is For If you are a budding data scientist or a data analyst who wants to analyze and gain actionable insights from data using Python, this book is for you. Programmers with some experience in Python who want to enter the lucrative world of Data Science will also find this book to be very useful, but you don't need to be an expert Python coder or mathematician to get the most from this book. What You Will Learn Learn how to clean your data and ready it for analysis Implement the popular clustering and regression methods in Python Train efficient machine learning models using decision trees and random forests Visualize the results of your analysis using Python's Matplotlib library Use Apache Spark's MLlib package to perform machine learning on large datasets In Detail Join Frank Kane, who worked on Amazon and IMDb's machine learning algorithms, as he guides you on your first steps into the world of data science. Hands-On Data Science and Python Machine Learning gives you the tools that you need to understand and explore the core topics in the field, and the confidence and practice to build and analyze your own machine learning models. With the help of interesting and easy-to-follow practical examples, Frank Kane explains potentially complex topics such as Bayesian methods and K-means clustering in a way that anybody can understand them. Based on Frank's successful data science course, Hands-On Data Science and Python Machine Learning empowers you to conduct data analysis and perform efficient machine learning using Python. Let Frank help you unearth the value in your data using the various data mining and data analysis techniques available in Python, and to develop efficient predictive models to predict future results. You will also learn how to perform large-scale machine learning on Big Data using Apache Spark. The book covers preparing your data for analysis, training machine learning models, and visualizing the final data analysis. Style and approach This comprehensive book is a perfect blend of theory and hands-on code examples in Python which can be used for your reference at any time.

Training Systems Using Python Statistical Modeling

Author : Curtis Miller
Publisher : Packt Publishing Ltd
ISBN 13 : 1838820647
Total Pages : 284 pages
Book Rating : 4.8/5 (388 download)

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Book Synopsis Training Systems Using Python Statistical Modeling by : Curtis Miller

Download or read book Training Systems Using Python Statistical Modeling written by Curtis Miller and published by Packt Publishing Ltd. This book was released on 2019-05-20 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Leverage the power of Python and statistical modeling techniques for building accurate predictive models Key FeaturesGet introduced to Python's rich suite of libraries for statistical modelingImplement regression, clustering and train neural networks from scratchIncludes real-world examples on training end-to-end machine learning systems in PythonBook Description Python's ease of use and multi-purpose nature has led it to become the choice of tool for many data scientists and machine learning developers today. Its rich libraries are widely used for data analysis, and more importantly, for building state-of-the-art predictive models. This book takes you through an exciting journey, of using these libraries to implement effective statistical models for predictive analytics. You’ll start by diving into classical statistical analysis, where you will learn to compute descriptive statistics using pandas. You will look at supervised learning, where you will explore the principles of machine learning and train different machine learning models from scratch. You will also work with binary prediction models, such as data classification using k-nearest neighbors, decision trees, and random forests. This book also covers algorithms for regression analysis, such as ridge and lasso regression, and their implementation in Python. You will also learn how neural networks can be trained and deployed for more accurate predictions, and which Python libraries can be used to implement them. By the end of this book, you will have all the knowledge you need to design, build, and deploy enterprise-grade statistical models for machine learning using Python and its rich ecosystem of libraries for predictive analytics. What you will learnUnderstand the importance of statistical modelingLearn about the various Python packages for statistical analysisImplement algorithms such as Naive Bayes, random forests, and moreBuild predictive models from scratch using Python's scikit-learn libraryImplement regression analysis and clusteringLearn how to train a neural network in PythonWho this book is for If you are a data scientist, a statistician or a machine learning developer looking to train and deploy effective machine learning models using popular statistical techniques, then this book is for you. Knowledge of Python programming is required to get the most out of this book.

A Primer on Scientific Programming with Python

Author : Hans Petter Langtangen
Publisher : Springer
ISBN 13 : 3662498871
Total Pages : 942 pages
Book Rating : 4.6/5 (624 download)

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Book Synopsis A Primer on Scientific Programming with Python by : Hans Petter Langtangen

Download or read book A Primer on Scientific Programming with Python written by Hans Petter Langtangen and published by Springer. This book was released on 2016-07-28 with total page 942 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book serves as a first introduction to computer programming of scientific applications, using the high-level Python language. The exposition is example and problem-oriented, where the applications are taken from mathematics, numerical calculus, statistics, physics, biology and finance. The book teaches "Matlab-style" and procedural programming as well as object-oriented programming. High school mathematics is a required background and it is advantageous to study classical and numerical one-variable calculus in parallel with reading this book. Besides learning how to program computers, the reader will also learn how to solve mathematical problems, arising in various branches of science and engineering, with the aid of numerical methods and programming. By blending programming, mathematics and scientific applications, the book lays a solid foundation for practicing computational science. From the reviews: Langtangen ... does an excellent job of introducing programming as a set of skills in problem solving. He guides the reader into thinking properly about producing program logic and data structures for modeling real-world problems using objects and functions and embracing the object-oriented paradigm. ... Summing Up: Highly recommended. F. H. Wild III, Choice, Vol. 47 (8), April 2010 Those of us who have learned scientific programming in Python ‘on the streets’ could be a little jealous of students who have the opportunity to take a course out of Langtangen’s Primer.” John D. Cook, The Mathematical Association of America, September 2011 This book goes through Python in particular, and programming in general, via tasks that scientists will likely perform. It contains valuable information for students new to scientific computing and would be the perfect bridge between an introduction to programming and an advanced course on numerical methods or computational science. Alex Small, IEEE, CiSE Vol. 14 (2), March /April 2012 “This fourth edition is a wonderful, inclusive textbook that covers pretty much everything one needs to know to go from zero to fairly sophisticated scientific programming in Python...” Joan Horvath, Computing Reviews, March 2015

Hands-On Unsupervised Learning with Python

Author : Giuseppe Bonaccorso
Publisher : Packt Publishing Ltd
ISBN 13 : 1789349273
Total Pages : 375 pages
Book Rating : 4.7/5 (893 download)

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Book Synopsis Hands-On Unsupervised Learning with Python by : Giuseppe Bonaccorso

Download or read book Hands-On Unsupervised Learning with Python written by Giuseppe Bonaccorso and published by Packt Publishing Ltd. This book was released on 2019-02-28 with total page 375 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the skill-sets required to implement various approaches to Machine Learning with Python Key FeaturesExplore unsupervised learning with clustering, autoencoders, restricted Boltzmann machines, and moreBuild your own neural network models using modern Python librariesPractical examples show you how to implement different machine learning and deep learning techniquesBook Description Unsupervised learning is about making use of raw, untagged data and applying learning algorithms to it to help a machine predict its outcome. With this book, you will explore the concept of unsupervised learning to cluster large sets of data and analyze them repeatedly until the desired outcome is found using Python. This book starts with the key differences between supervised, unsupervised, and semi-supervised learning. You will be introduced to the best-used libraries and frameworks from the Python ecosystem and address unsupervised learning in both the machine learning and deep learning domains. You will explore various algorithms, techniques that are used to implement unsupervised learning in real-world use cases. You will learn a variety of unsupervised learning approaches, including randomized optimization, clustering, feature selection and transformation, and information theory. You will get hands-on experience with how neural networks can be employed in unsupervised scenarios. You will also explore the steps involved in building and training a GAN in order to process images. By the end of this book, you will have learned the art of unsupervised learning for different real-world challenges. What you will learnUse cluster algorithms to identify and optimize natural groups of dataExplore advanced non-linear and hierarchical clustering in actionSoft label assignments for fuzzy c-means and Gaussian mixture modelsDetect anomalies through density estimationPerform principal component analysis using neural network modelsCreate unsupervised models using GANsWho this book is for This book is intended for statisticians, data scientists, machine learning developers, and deep learning practitioners who want to build smart applications by implementing key building block unsupervised learning, and master all the new techniques and algorithms offered in machine learning and deep learning using real-world examples. Some prior knowledge of machine learning concepts and statistics is desirable.

Python for Finance Cookbook

Author : Eryk Lewinson
Publisher : Packt Publishing Ltd
ISBN 13 : 1789617324
Total Pages : 426 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Python for Finance Cookbook by : Eryk Lewinson

Download or read book Python for Finance Cookbook written by Eryk Lewinson and published by Packt Publishing Ltd. This book was released on 2020-01-31 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve common and not-so-common financial problems using Python libraries such as NumPy, SciPy, and pandas Key FeaturesUse powerful Python libraries such as pandas, NumPy, and SciPy to analyze your financial dataExplore unique recipes for financial data analysis and processing with PythonEstimate popular financial models such as CAPM and GARCH using a problem-solution approachBook Description Python is one of the most popular programming languages used in the financial industry, with a huge set of accompanying libraries. In this book, you'll cover different ways of downloading financial data and preparing it for modeling. You'll calculate popular indicators used in technical analysis, such as Bollinger Bands, MACD, RSI, and backtest automatic trading strategies. Next, you'll cover time series analysis and models, such as exponential smoothing, ARIMA, and GARCH (including multivariate specifications), before exploring the popular CAPM and the Fama-French three-factor model. You'll then discover how to optimize asset allocation and use Monte Carlo simulations for tasks such as calculating the price of American options and estimating the Value at Risk (VaR). In later chapters, you'll work through an entire data science project in the financial domain. You'll also learn how to solve the credit card fraud and default problems using advanced classifiers such as random forest, XGBoost, LightGBM, and stacked models. You'll then be able to tune the hyperparameters of the models and handle class imbalance. Finally, you'll focus on learning how to use deep learning (PyTorch) for approaching financial tasks. By the end of this book, you’ll have learned how to effectively analyze financial data using a recipe-based approach. What you will learnDownload and preprocess financial data from different sourcesBacktest the performance of automatic trading strategies in a real-world settingEstimate financial econometrics models in Python and interpret their resultsUse Monte Carlo simulations for a variety of tasks such as derivatives valuation and risk assessmentImprove the performance of financial models with the latest Python librariesApply machine learning and deep learning techniques to solve different financial problemsUnderstand the different approaches used to model financial time series dataWho this book is for This book is for financial analysts, data analysts, and Python developers who want to learn how to implement a broad range of tasks in the finance domain. Data scientists looking to devise intelligent financial strategies to perform efficient financial analysis will also find this book useful. Working knowledge of the Python programming language is mandatory to grasp the concepts covered in the book effectively.

Pyomo – Optimization Modeling in Python

Author : William E. Hart
Publisher : Springer Science & Business Media
ISBN 13 : 146143226X
Total Pages : 245 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Pyomo – Optimization Modeling in Python by : William E. Hart

Download or read book Pyomo – Optimization Modeling in Python written by William E. Hart and published by Springer Science & Business Media. This book was released on 2012-02-15 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a complete and comprehensive reference/guide to Pyomo (Python Optimization Modeling Objects) for both beginning and advanced modelers, including students at the undergraduate and graduate levels, academic researchers, and practitioners. The text illustrates the breadth of the modeling and analysis capabilities that are supported by the software and support of complex real-world applications. Pyomo is an open source software package for formulating and solving large-scale optimization and operations research problems. The text begins with a tutorial on simple linear and integer programming models. A detailed reference of Pyomo's modeling components is illustrated with extensive examples, including a discussion of how to load data from data sources like spreadsheets and databases. Chapters describing advanced modeling capabilities for nonlinear and stochastic optimization are also included. The Pyomo software provides familiar modeling features within Python, a powerful dynamic programming language that has a very clear, readable syntax and intuitive object orientation. Pyomo includes Python classes for defining sparse sets, parameters, and variables, which can be used to formulate algebraic expressions that define objectives and constraints. Moreover, Pyomo can be used from a command-line interface and within Python's interactive command environment, which makes it easy to create Pyomo models, apply a variety of optimizers, and examine solutions. The software supports a different modeling approach than commercial AML (Algebraic Modeling Languages) tools, and is designed for flexibility, extensibility, portability, and maintainability but also maintains the central ideas in modern AMLs.

Simulation Modeling and Analysis with Expertfit Software

Author : Averill Law
Publisher : McGraw-Hill Science/Engineering/Math
ISBN 13 : 9780073294414
Total Pages : 792 pages
Book Rating : 4.2/5 (944 download)

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Book Synopsis Simulation Modeling and Analysis with Expertfit Software by : Averill Law

Download or read book Simulation Modeling and Analysis with Expertfit Software written by Averill Law and published by McGraw-Hill Science/Engineering/Math. This book was released on 2006-07-21 with total page 792 pages. Available in PDF, EPUB and Kindle. Book excerpt: Since the publication of the first edition in 1982, the goal of Simulation Modeling and Analysis has always been to provide a comprehensive, state-of-the-art, and technically correct treatment of all important aspects of a simulation study. The book strives to make this material understandable by the use of intuition and numerous figures, examples, and problems. It is equally well suited for use in university courses, simulation practice, and self study. The book is widely regarded as the “bible” of simulation and now has more than 100,000 copies in print. The book can serve as the primary text for a variety of courses; for example: • A first course in simulation at the junior, senior, or beginning-graduate-student level in engineering, manufacturing, business, or computer science (Chaps. 1 through 4, and parts of Chaps. 5 through 9). At the end of such a course, the students will be prepared to carry out complete and effective simulation studies, and to take advanced simulation courses. • A second course in simulation for graduate students in any of the above disciplines (most of Chaps. 5 through 12). After completing this course, the student should be familiar with the more advanced methodological issues involved in a simulation study, and should be prepared to understand and conduct simulation research. • An introduction to simulation as part of a general course in operations research or management science (part of Chaps. 1, 3, 5, 6, and 9).

Programming for Computations - Python

Author : Svein Linge
Publisher : Springer
ISBN 13 : 3319324284
Total Pages : 244 pages
Book Rating : 4.3/5 (193 download)

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Book Synopsis Programming for Computations - Python by : Svein Linge

Download or read book Programming for Computations - Python written by Svein Linge and published by Springer. This book was released on 2016-07-25 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents computer programming as a key method for solving mathematical problems. There are two versions of the book, one for MATLAB and one for Python. The book was inspired by the Springer book TCSE 6: A Primer on Scientific Programming with Python (by Langtangen), but the style is more accessible and concise, in keeping with the needs of engineering students. The book outlines the shortest possible path from no previous experience with programming to a set of skills that allows the students to write simple programs for solving common mathematical problems with numerical methods in engineering and science courses. The emphasis is on generic algorithms, clean design of programs, use of functions, and automatic tests for verification.

Derivatives Analytics with Python

Author : Yves Hilpisch
Publisher : John Wiley & Sons
ISBN 13 : 1119037999
Total Pages : 390 pages
Book Rating : 4.1/5 (19 download)

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Book Synopsis Derivatives Analytics with Python by : Yves Hilpisch

Download or read book Derivatives Analytics with Python written by Yves Hilpisch and published by John Wiley & Sons. This book was released on 2015-08-03 with total page 390 pages. Available in PDF, EPUB and Kindle. Book excerpt: Supercharge options analytics and hedging using the power of Python Derivatives Analytics with Python shows you how to implement market-consistent valuation and hedging approaches using advanced financial models, efficient numerical techniques, and the powerful capabilities of the Python programming language. This unique guide offers detailed explanations of all theory, methods, and processes, giving you the background and tools necessary to value stock index options from a sound foundation. You'll find and use self-contained Python scripts and modules and learn how to apply Python to advanced data and derivatives analytics as you benefit from the 5,000+ lines of code that are provided to help you reproduce the results and graphics presented. Coverage includes market data analysis, risk-neutral valuation, Monte Carlo simulation, model calibration, valuation, and dynamic hedging, with models that exhibit stochastic volatility, jump components, stochastic short rates, and more. The companion website features all code and IPython Notebooks for immediate execution and automation. Python is gaining ground in the derivatives analytics space, allowing institutions to quickly and efficiently deliver portfolio, trading, and risk management results. This book is the finance professional's guide to exploiting Python's capabilities for efficient and performing derivatives analytics. Reproduce major stylized facts of equity and options markets yourself Apply Fourier transform techniques and advanced Monte Carlo pricing Calibrate advanced option pricing models to market data Integrate advanced models and numeric methods to dynamically hedge options Recent developments in the Python ecosystem enable analysts to implement analytics tasks as performing as with C or C++, but using only about one-tenth of the code or even less. Derivatives Analytics with Python — Data Analysis, Models, Simulation, Calibration and Hedging shows you what you need to know to supercharge your derivatives and risk analytics efforts.

Understanding Optics with Python

Author : Vasudevan Lakshminarayanan
Publisher : CRC Press
ISBN 13 : 1351648845
Total Pages : 342 pages
Book Rating : 4.3/5 (516 download)

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Book Synopsis Understanding Optics with Python by : Vasudevan Lakshminarayanan

Download or read book Understanding Optics with Python written by Vasudevan Lakshminarayanan and published by CRC Press. This book was released on 2018-02-19 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt: Optics is an enabling science that forms a basis for our technological civilization. Courses in optics are a required part of the engineering or physics undergraduate curriculum in many universities worldwide. The aim of Understanding Optics with Python is twofold: first, to describe certain basic ideas of classical physical and geometric optics; second, to introduce the reader to computer simulations of physical phenomena. The text is aimed more broadly for those who wish to use numerical/computational modeling as an educational tool that promotes interactive teaching (and learning). In addition, it offers an alternative to developing countries where the necessary equipment to carry out the appropriate experiments is not available as a result of financial constraints. This approach contributes to a better diffusion of knowledge about optics. The examples given in this book are comparable to those found in standard textbooks on optics and are suitable for self-study. This text enables the user to study and understand optics using hands-on simulations with Python. Python is our programming language of choice because of its open-source availability, extensive functionality, and an enormous online support. Essentials of programming in Python 3.x, including graphical user interface, are also provided. The codes in the book are available for download on the book’s website. Discusses most standard topics of traditional physical and geometrical optics through Python and PyQt5 Provides visualizations and in-depth descriptions of Python’s programming language and simulations Includes simulated laboratories where students are provided a "hands-on" exploration of Python software Coding and programming featured within the text are available for download on the book’s corresponding website. "Understanding Optics with Python by Vasudevan Lakshminarayanan, Hassen Ghalila, Ahmed Ammar, and L. Srinivasa Varadharajan is born around a nice idea: using simulations to provide the students with a powerful tool to understand and master optical phenomena. The choice of the Python language is perfectly matched with the overall goal of the book, as the Python language provides a completely free and easy-to-learn platform with huge cross-platform compatibility, where the reader of the book can conduct his or her own numerical experiments to learn faster and better." — Costantino De Angelis, University of Brescia, Italy "Teaching an important programming language like Python through concrete examples from optics is a natural and, in my view, very effective approach. I believe that this book will be used by students and appreciated greatly by instructors. The topic of modelling optical effects and systems where the students should already have a physical background provides great motivation for students to learn the basics of a powerful programming language without the intimidation factor that often goes with a formal computer science course." — John Dudley, FEMTO-ST Institute, Besançon, France

Introduction to Computation and Programming Using Python, second edition

Author : John V. Guttag
Publisher : MIT Press
ISBN 13 : 0262529629
Total Pages : 466 pages
Book Rating : 4.2/5 (625 download)

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Book Synopsis Introduction to Computation and Programming Using Python, second edition by : John V. Guttag

Download or read book Introduction to Computation and Programming Using Python, second edition written by John V. Guttag and published by MIT Press. This book was released on 2016-08-12 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: The new edition of an introductory text that teaches students the art of computational problem solving, covering topics ranging from simple algorithms to information visualization. This book introduces students with little or no prior programming experience to the art of computational problem solving using Python and various Python libraries, including PyLab. It provides students with skills that will enable them to make productive use of computational techniques, including some of the tools and techniques of data science for using computation to model and interpret data. The book is based on an MIT course (which became the most popular course offered through MIT's OpenCourseWare) and was developed for use not only in a conventional classroom but in in a massive open online course (MOOC). This new edition has been updated for Python 3, reorganized to make it easier to use for courses that cover only a subset of the material, and offers additional material including five new chapters. Students are introduced to Python and the basics of programming in the context of such computational concepts and techniques as exhaustive enumeration, bisection search, and efficient approximation algorithms. Although it covers such traditional topics as computational complexity and simple algorithms, the book focuses on a wide range of topics not found in most introductory texts, including information visualization, simulations to model randomness, computational techniques to understand data, and statistical techniques that inform (and misinform) as well as two related but relatively advanced topics: optimization problems and dynamic programming. This edition offers expanded material on statistics and machine learning and new chapters on Frequentist and Bayesian statistics.