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Probability and Statistics for Computer Scientists

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Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools
Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses. Written in a lively style with simple language, this classroom-tested book can now be used in both one- and two-semester courses.

New to the Second Edition

  • Axiomatic introduction of probability
  • Expanded coverage of statistical inference, including standard errors of estimates and their estimation, inference about variances, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap
  • More exercises at the end of each chapter
  • Additional MATLAB® codes, particularly new commands of the Statistics Toolbox

In-Depth yet Accessible Treatment of Computer Science-Related Topics
Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET).

Encourages Practical Implementation of Skills
Using simple MATLAB commands (easily translatable to other computer languages), the book provides short programs for implementing the methods of probability and statistics as well as for visualizing randomness, the behavior of random variables and stochastic processes, convergence results, and Monte Carlo simulations. Preliminary knowledge of MATLAB is not required. Along with numerous computer science applications and worked examples, the text presents interesting facts and paradoxical statements. Each chapter concludes with a short summary and many exercises.

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Analyzing Data Through Probabilistic Modeling in Statistics
Analyzing Data Through Probabilistic Modeling in Statistics
"This book addresses different aspects of probabilistic modeling, stochastic methods, probabilistic distributions, data analysis, optimization methods, and probabilistic methods in risk analysis"--...
Bootstrapping Microservices with Docker, Kubernetes, and Terraform: A project-based guide
Bootstrapping Microservices with Docker, Kubernetes, and Terraform: A project-based guide
Summary
The best way to learn microservices development is to build something! Bootstrapping Microservices with Docker, Kubernetes, and Terraform guides you from zero through to a complete microservices project, including fast prototyping, development, and deployment. You’ll get your feet wet using
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Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning
Data Science Revealed: With Feature Engineering, Data Visualization, Pipeline Development, and Hyperparameter Tuning
This book introduces you to the world of data science. It reveals the proper way to do data science. It covers essential statistical and programming techniques to help you understand data science from a broad perspective. Not only that, but it provides a theoretical, technical, and mathematical foundation for problem-solving...

Practical Statistics for Data Scientists: 50 Essential Concepts
Practical Statistics for Data Scientists: 50 Essential Concepts

Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their...

Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition
Docker Cookbook: Over 100 practical and insightful recipes to build distributed applications with Docker , 2nd Edition

Leverage Docker to deploying software at scale

Key Features

  • Leverage practical examples to manage containers efficiently
  • Integrate with orchestration tools such as Kubernetes for controlled deployments
  • Learn to implement best practices on improving...
Hands-on Kubernetes on Azure: Use Azure Kubernetes Service to automate management, scaling, and deployment of containerized applications, 3rd Edition
Hands-on Kubernetes on Azure: Use Azure Kubernetes Service to automate management, scaling, and deployment of containerized applications, 3rd Edition

Understand the fundamentals of Kubernetes deployment on Azure with a learn-by-doing approach

Key Features

  • Get to grips with the fundamentals of containers and Kubernetes
  • Deploy containerized applications using the Kubernetes platform
  • Learn how you can...
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