Mel Slater – författare

This book walks you through learning probability and statistics from a Bayesian point of view.  From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with data addressing social and psychological issues.

The book also:

Equips you with coding skills in the statistical modelling language Stan and programming language R. Discusses how Bayesian approaches to statistics compare to classical approaches. Introduces Markov Chain Monte Carlo methods for doing Bayesian statistics through computer simulations, so you understand how Bayesian solutions are implemented.

Features include an introduction to each chapter and a chapter summary to help you check your learning. All the examples and data used in the book are also available in the online resources so you can practice at your own pace. For readers with some understanding of basic mathematical functions and notation, this book will get you up and running so you can do Bayesian statistics with confidence.

This book walks you through learning probability and statistics from a Bayesian point of view.  From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with data addressing social and psychological issues.

The book also:

Equips you with coding skills in the statistical modelling language Stan and programming language R. Discusses how Bayesian approaches to statistics compare to classical approaches. Introduces Markov Chain Monte Carlo methods for doing Bayesian statistics through computer simulations, so you understand how Bayesian solutions are implemented.

Features include an introduction to each chapter and a chapter summary to help you check your learning. All the examples and data used in the book are also available in the online resources so you can practice at your own pace. For readers with some understanding of basic mathematical functions and notation, this book will get you up and running so you can do Bayesian statistics with confidence.

This book walks you through learning probability and statistics from a Bayesian point of view.  From an introduction to probability theory through to frameworks for doing rigorous calculations of probability, it discusses Bayes’ Theorem before illustrating how to use it in a variety of different situations with data addressing social and psychological issues.

The book also:

Equips you with coding skills in the statistical modelling language Stan and programming language R. Discusses how Bayesian approaches to statistics compare to classical approaches. Introduces Markov Chain Monte Carlo methods for doing Bayesian statistics through computer simulations, so you understand how Bayesian solutions are implemented.

Features include an introduction to each chapter and a chapter summary to help you check your learning. All the examples and data used in the book are also available in the online resources so you can practice at your own pace. For readers with some understanding of basic mathematical functions and notation, this book will get you up and running so you can do Bayesian statistics with confidence.

This book constitutes the refereed proceedings of the 14th International Conference on Virtual Reality and Augmented Reality, EuroVR 2017, held in Laval, France, in December 2017.The 10 full papers and 2 short papers presented were carefully reviewed and selected from 36 submissions. The papers are organized in four topical sections: interaction models and user studies, visual and haptic real-time rendering, perception and cognition, and rehabilitation and safety.