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Diagrammatic Reasoning in AI
Diagrammatic Reasoning in AI

This book is really the end product of over a decade of work, on and off, on diagrammatic reasoning in artificial intelligence (AI). In developing this book, I drew inspiration from a variety of sources: two experimental studies, the development of two prototype systems, an extensive literature review and analysis in AI,...

Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Bayesian Statistics the Fun Way: Understanding Statistics and Probability with Star Wars, LEGO, and Rubber Ducks
Fun guide to learning Bayesian statistics and probability through unusual and illustrative examples.

Probability and statistics are increasingly important in a huge range of professions. But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian
...
Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
This book is intended to have three roles and to serve three associated audiences: an introductory text on Bayesian inference star ting from first principles, a graduate text on effective current approaches to Bayesian modeling and computation in statistics and related fields, and a handbook of Bayesian meth ods in applied...
Statistical Bioinformatics: with R
Statistical Bioinformatics: with R

Bioinformatics is an emerging field in which statistical and computational techniques are used extensively to analyze and interpret biological data obtained from high-throughput genomic technologies. Genomic technologies allow us to monitor thousands of biological processes going on inside living organisms in one snapshot, and are...

R Deep Learning Essentials
R Deep Learning Essentials

Key Features

  • Harness the ability to build algorithms for unsupervised data using deep learning concepts with R
  • Master the common problems faced such as overfitting of data, anomalous datasets, image recognition, and performance tuning while building the models
  • Build models relating to neural...
Hidden Markov Models for Time Series: An Introduction Using R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
Hidden Markov Models for Time Series: An Introduction Using R (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)

Reveals How HMMs Can Be Used as General-Purpose Time Series Models

Implements all methods in R
Hidden Markov Models for Time Series: An Introduction Using R applies hidden Markov models (HMMs) to a wide range of time series types, from continuous-valued, circular, and
...

Bayesian Biostatistics and Diagnostic Medicine
Bayesian Biostatistics and Diagnostic Medicine
Bayesian methods are being used more often than ever before in biology and medicine. For example, at the University of Texas MD Anderson Cancer Center, Bayesian sequential stopping rules routinely are used for the design of clinical trials. This book is based on the author’s experience working with a variety of...
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
We live in a world that is rich in data, ever increasing in scale. This data comes from many di erent sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer databases, nancial transactions, engine monitoring, speech recognition, surveillance, search). Possessing the knowledge as to...
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past decade, and this book provides a long-needed systematic and unified treatment of theoretical and practical aspects of GPs in machine learning. The...
Bayesian Time Series Models
Bayesian Time Series Models

'What's going to happen next?' Time series data hold the answers, and Bayesian methods represent the cutting edge in learning what they have to say. This ambitious book is the first unified treatment of the emerging knowledge-base in Bayesian time series techniques. Exploiting the unifying framework of probabilistic graphical...

Bayesian Artificial Intelligence, Second Edition
Bayesian Artificial Intelligence, Second Edition

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...

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis
Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis

Introduction to Statistical Decision Theory: Utility Theory and Causal Analysis provides the theoretical background to approach decision theory from a statistical perspective. It covers both traditional approaches, in terms of value theory and expected utility theory, and recent developments, in terms of causal...

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