This book offers a comprehensive discussion of the Bayesian inference framework and demonstrates why this probabilistic approach is ideal for tackling the various modelling pro...Loe edasi...
This presents recent advancements in probabilistic geotechnical site characterization. It reviews probability theories and models for cross correlation and spatial correlation, and presents methods for Bayesian parameter estimation and prediction....Loe edasi...
This book is designed to make spatio-temporal modeling and analysis understandable to students and researchers, mathematicians and statisticians and practitioners in the applied sciences. By avoiding hardcore math and calculus, this book aims to be a...Loe edasi...
This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.The book is divided into four parts. The first part reviews the theoretical...Loe edasi...
Apply advanced techniques for optimizing machine learning processes. Bayesian optimization helps pinpoint the best configuration for your machine learning models with speed and accuracy.In Bayesian Optimization in Action you wil...Loe edasi...
This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the structure within the data to be visualized, and thereby make test results useful for students, teachers, and the...Loe edasi...
Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex inte...Loe edasi...
This book covers the essential theory and implementation of popular Bayesian optimization techniques in an intuitive and well-illustrated manner. The techniques covered in this book will enable you to better tune the hyperparemeters of your machin...Loe edasi...
This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language.The book is divided into four parts. The first part reviews the theoretical...Loe edasi...
The objective is to prepare students and scientists to analyze genomics data using empirical Bayes methods and to critically evaluate the statistical methods appearing in genomics articles. That is accomplished by providing the information needed...Loe edasi...
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 discove...Loe edasi...
A new modeling methodology, new inference algorithms, new programming languages, and new hardware are all needed to create a complete Bayesian computing framework. Focusing on the methodology and algorithms, this book describes the first steps tow...Loe edasi...
This is the first technical book that considers tests as public tools and examines how to engineer and process test data, extract the structure within the data to be visualized, and thereby make test results useful for students, teachers, and the...Loe edasi...
The book Bayesian Reasoning and Gaussian Processes for Machine Learning Applications talks about Bayesian Reasoning and Gaussian Processes in machine learning applications. Bayesian methods are applied in many areas such as game development, decisio...Loe edasi...
This book is designed to make spatio-temporal modeling and analysis understandable to students and researchers, mathematicians and statisticians and practitioners in the applied sciences. By avoiding hardcore math and calculus, this book aims to b...Loe edasi...
(Ilmumisaeg: 24-Jun-2021, Hardback, Kirjastus: Cambridge University Press, ISBN-13: 9781108835565)
This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can u...Loe edasi...
This textbook teaches undergraduates in psychology, neuroscience, and medicine modern data analysis techniques. It uses non-calculus-based mathematics with examples specific to behavioral and neural sciences. Perfect for statistics courses, it shows...Loe edasi...
If you know how to program with Python, youre ready to tackle Bayesian statistics. With this book, youll learn how to solve statistical problems with Python code instead of mathematical formulas, using discrete probability distributions ins...Loe edasi...
Progressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex inte...Loe edasi...
