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Making Software - What Really Works, and Why We Believe It. Does the MMR vaccine cause autism? Does watching violence on TV make children more violent? Are some programming languages better than others? People argue about these questions every day. Every serious attempt to answer the first two questions relies on the scientific method: careful collection of evidence, and impartial evaluation of its implications. Until recently, though, only a few people have tried to apply these techniques to the third. When it comes to computing, it often seems that a couple glasses of beer and an anecdote about a startup in Warsaw are all the “evidence” most programmers expect. That is changing, thanks in part to the work of the contributors to this book. Drawing on fields as diverse as data mining, cognitive psychology, and sociology, they and their colleagues are creating an evidence-based approach to software engineering. By gathering evidence drawn from a myriad of primary sources and analyzing the results, they are shedding new light onto some vexing questions of software development. What do most programmers get wrong in their first job? Does test-driven development lead to better code? What about pair programming, or code reviews? Is it possible to predict the likely number of bugs in a piece of code before it’s released? If so, how?

Decision Making Under Uncertainty Theory and Application. 2015 By Mykel J. Kochenderfer With Christopher Amato, Girish Chowdhary, Jonathan P. How, Hayley J. Davison Reynolds, Jason R. Thornton, Pedro A. Torres-Carrasquillo, N. Kemal Üre and John Vian Overview Many important problems involve decision making under uncertainty—that is, choosing actions based on often imperfect observations, with unknown outcomes. Designers of automated decision support systems must take into account the various sources of uncertainty while balancing the multiple objectives of the system. This book provides an introduction to the challenges of decision making under uncertainty from a computational perspective. It presents both the theory behind decision making models and algorithms and a collection of example applications that range from speech recognition to aircraft collision avoidance. Focusing on two methods for designing decision agents, planning and reinforcement learning, the book covers probabilistic models, introducing Bayesian networks as a graphical model that captures probabilistic relationships between variables; utility theory as a framework for understanding optimal decision making under uncertainty; Markov decision processes as a method for modeling sequential problems; model uncertainty; state uncertainty; and cooperative decision making involving multiple interacting agents. A series of applications shows how the theoretical concepts can be applied to systems for attribute-based person search, speech applications, collision avoidance, and unmanned aircraft persistent surveillance. Decision Making Under Uncertainty unifies research from different communities using consistent notation, and is accessible to students and researchers across engineering disciplines who have some prior exposure to probability theory and calculus. It can be used as a text for advanced undergraduate and graduate students in fields including computer science, aerospace and electrical enginee

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创造形式 | 英语 FormMaking在和基础上开发，配备了最新的前端技术堆栈，内置的i18n国际化解决方案，所有这些旨在使开发变得更简单，更有效。 （根据视觉操作快速设计表单页面。） （生成器将基于设计器中捕获的配置json数据快速呈现表单页面。） 该项目是基本版本，如果您需要体验Advanced ，则可以转到高级版本，该版本提供了更多的组件和功能。 特征 视觉配置页面 提供网格布局并与flex对齐 一键式预览配置效果 一键生成配置json数据 一键生成代码，准备运行 提供自定义组件以满足用户的自定义要求 提供一个远程数据接口，供用户异步获取数据 提供强大的高级组件 支持表单验证 快速获取表格数据 国际化支持 第三方插件 可拖曳的 元素用户界面 高手 vue2编辑器 浏览器支持 现代浏览器和Internet Explorer 10+。 IE浏览器/边缘 火狐浏览器

Casuality, Correlation and Artificial Intelligence for Rational Decision Making By 作者: Tshilidzi Marwala ISBN-10 书号: 9814630861 ISBN-13 书号: 9789814630863 Release Finelybook 出版日期: March 5, 2015 pages 页数: (208)

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