Learn IT - Books tags inference

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

Springer, 2019

This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource...

Reasoning with Probabilistic and Deterministic Graphical Models: Exact Algorithms (Synthesis Lectures on Artificial Intelligence and Machine Learning)

Morgan and Claypool Publishers, 2013

Graphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and...

Inference Control in Statistical Databases: From Theory to Practice (Lecture Notes in Computer Science)

Springer, 2002

Inference control in statistical databases, also known as statistical disclosure limitation or statistical confidentiality, is about finding tradeoffs to the tension between the increasing societal need for accurate statistical data and the legal and ethical obligation to protect privacy of individuals and enterprises which are the source...

Bayesian Artificial Intelligence, Second Edition

CRC Press, 2010

Updated and expanded, Bayesian Artificial Intelligence, Second Edition provides a practical and accessible introduction to the main concepts, foundation, and applications of Bayesian networks. It focuses on both the causal discovery of networks and Bayesian inference procedures. Adopting a causal interpretation of Bayesian...

Computer Age Statistical Inference: Algorithms, Evidence, and Data Science (Institute of Mathematical Statistics Monographs)

Cambridge University Press, 2016

The twenty-first century has seen a breathtaking expansion of statistical methodology, both in scope and in influence. “Big data,” “data science,” and “machine learning” have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science...

Probability and Statistics for Computer Scientists

CRC Press, 2013

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...

5 Steps to a 5 AP Statistics, 2010-2011 Edition (5 Steps to a 5 on the Advanced Placement Examinations Series)

McGraw-Hill, 2009

A Perfect Plan for the Perfect Score

We want you to succeed on your AP* exam. That's why we've created this 5-step plan to help you study more effectively, use your preparation time wisely, and get your best score. This easy-to-follow guide offers you a complete review of your AP course, strategies to give you the edge on test...

Mathematical Models of Spoken Language

John Wiley & Sons, 2003

Humans use language to convey meaningful messages to each other. Linguistic competence consists in the ability to express meaning reliably, not simply to obtain faithful lexical transcriptions. This invaluable reference tool is the product of many years' experience and research on language and speech technology. It presents the motivations for,...

Think Stats

O'Reilly, 2011

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the...

Markov Random Fields for Vision and Image Processing (MIT Press)

MIT Press, 2011

This volume demonstrates the power of the Markov random field (MRF) in vision, treating the MRF both as a tool for modeling image data and, utilizing recently developed algorithms, as a means of making inferences about images. These inferences concern underlying image and scene structure as well as solutions to such problems as image...

Building Probabilistic Graphical Models with Python

Packt Publishing, 2014

Solve machine learning problems using probabilistic graphical models implemented in Python with real-world applications

About This Book

Stretch the limits of machine learning by learning how graphical models provide an insight on particular problems, especially in high dimension areas such as image...

Swift Pocket Reference

O'Reilly, 2014

Get quick answers for developing and debugging applications with Swift, Apple’s multi-paradigm programming language. This pocket reference is the perfect on-the-job tool for learning Swift’s modern language features, including type safety, generics, type inference, closures, tuples, automatic memory management, and...

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