Think Bayes
If you know how to program with Python and also know a little about probability, you're ready to tackle Bayesian statistics. With this book, you'll learn how to solve statistical problems with Python code instead of mathematical notation, and use discrete probability distributions instead of continuous mathematics. Once you get the math out of the ...
Bayesian Methods in the Search for MH370
This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths. It provides details of how the probabilistic models of aircraft flight dynamics, satellite communication system measurements, environmental effects and radar data were constructed...
Evolution, Monitoring and Predicting Models of Rockburst
This open book focuses on investigating predicting precursor information and key points of rockburst in mining engineering through laboratory experiment, theoretical analysis, numerical simulation and case studies. Understanding the evolution patterns for the microstructure instability of rock is a prerequisite for rockburst prediction. The book pr...
An Introduction to Machine Learning
This textbook presents fundamental machine learning concepts in an easy to understand manner by providing practical advice, using straightforward examples, and offering engaging discussions of relevant applications. The main topics include Bayesian classifiers, nearest-neighbor classifiers, linear and polynomial classifiers, decision trees, neural ...
Seeing Theory
Statistics is quickly becoming the most important and multi-disciplinary field of mathematics. According to the American Statistical Association, statistician is one of the top ten fastest-growing occupations and statistics is one of the fastest-growing bachelor degrees. Statistical literacy is essential to our data driven society. Despite the ...
IMPROVE - Innovative Modelling Approaches for Production Systems to Raise Validatable Efficiency
This open access work presents selected results from the European research and innovation project IMPROVE which yielded novel data-based solutions to enhance machine reliability and efficiency in the fields of simulation and optimization, condition monitoring, alarm management, and quality prediction. Prof. Dr. Oliver Niggemann is Professor for Ar...
Evolutionary Genomics
This book addresses the challenge of analyzing and understanding the evolutionary dynamics of complex biological systems at the genomic level, and elaborates on some promising strategies that would bring us closer to uncovering of the vital relationships between genotype and phenotype. After a few educational primers, the book continues with sectio...
Theoretical and Practical Advances in Computer-based Educational Measurement
This book presents a multitude of different, yet related, innovations in educational measurement and provides insight in their practical possibilities.The book not only addresses several improvements in the quality of educational measurement and innovations in (inter)national large scale assessments, but also several advances in psychometrics and i...
Automated Machine Learning
This book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created ...
Theory of Statistics
This book is directed toward students for whom mathematical statistics is or will become an important part of their lives. Obviously, such students should be able to work through the details of 'hard' proofs and derivations. In addition, students at this level should acquire, or begin acquiring, a deep appreciation for the field, including its hist...
Probability and Statistics for Computer Science
This book is aimed at computer science undergraduates late in sophomore or early in junior year, supplying a comprehensive background in qualitative and quantitative data analysis, probability, random variables, and statistical methods, including machine learning.With careful treatment of topics that fill the curricular needs for the course, Probab...
Computational Cognitive Modeling and Linguistic Theory
This open book introduces a general framework that allows natural language researchers to enhance existing competence theories with fully specified performance and processing components. Gradually developing increasingly complex and cognitively realistic competence-performance models, it provides running code for these models and shows how to fit t...