Introduction to Statistical Inference

This textbook offers a thorough and practical introduction to inductive logic. The book covers a range of different types of inferences with an emphasis throughout on rep

An introduction to computational modeling for cognitive neuroscientists, covering both foundational work and recent developments. Cognitive neuroscientists need sophisticated conceptual tools to make sense of their field’s proliferation of novel theories, methods, and data. Computational modeling is such a tool, enabling researchers to turn theories into precise formulations. This book offers a mathematically gentle and theoretically unified introduction to modeling cognitive processes. Theoretical exercises of varying degrees of difficulty throughout help readers develop their modeling skills. After a general introduction to cognitive modeling and optimization, the book covers models of decision making; supervised learning algorithms, including Hebbian learning, delta rule, and backpropagation; the statistical model analysis methods of model parameter estimation and model evaluation; the three recent cognitive modeling approaches of reinforcement learning, unsupervised learning, and

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different

The Bayesian revolution in statistics - where statistics is integrated with decisionmaking in areas such as management, public policy, engineering, and clinical medicine - is here tostay. Introduction

Advanced statistical modeling and knowledge representation techniques for a newlyemerging area of machine learning and probabilistic reasoning; includes introductory material,tutorials for different p

An introduction to the Bayesian approach to statistical inference that demonstrates its superiority to orthodox frequentist statistical analysis.This book offers an introduction to the Bayesian approa

According to the authors, the Bayesian revolution in statistics - where statistics is integrated with decision making in areas such as management, public policy, engineering, and clinical medicine - i

A basic text on the theoretical foundations of information theory, for graduate students and engineers interested in electrical communications and for others seeking a general introduction to the fiel

The second and expanded edition of a comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.This textbook offers a comprehensive and self-co

A comprehensive introduction to Support Vector Machines and related kernel methods. In the 1990s, a new type of learning algorithm was developed, based on results from statistical learning theory: t