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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)
Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference (Addison-Wesley Data & Analytics)

Master Bayesian Inference through Practical Examples and Computation–Without Advanced Mathematical Analysis

 

Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely...

Mobile Robot Navigation with Intelligent Infrared Image Interpretation
Mobile Robot Navigation with Intelligent Infrared Image Interpretation

Mobile robots require the ability to make decisions such as "go through the hedges" or "go around the brick wall." Mobile Robot Navigation with Intelligent Infrared Image Interpretation describes in detail an alternative to GPS navigation: a physics-based adaptive Bayesian pattern classification model that uses a...

Optimisation in Signal and Image Processing
Optimisation in Signal and Image Processing

This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic...

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,...

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...
Biomedical Image Analysis: Tracking (Synthesis Lectures on Image, Video, & Multimedia Processing)
Biomedical Image Analysis: Tracking (Synthesis Lectures on Image, Video, & Multimedia Processing)
In biological and medical imaging applications, tracking objects in motion is a critical task. This book describes the state-of-the-art in biomedical tracking techniques. We begin by detailing methods for tracking using active contours, which have been highly successful in biomedical applications. The book next covers the major probabilistic...
Advanced Image Processing in Magnetic Resonance Imaging (Signal Processing and Communications)
Advanced Image Processing in Magnetic Resonance Imaging (Signal Processing and Communications)
Magnetic Resonance (MR) imaging produces images of the human tissues in a noninvasive manner, revealing the structure, metabolism, and function of tissues and organs. The impact of this image technique in diagnostic radiology is impressive, due to its versatility and flexibility in joining high-quality anatomical images with functional...
Modeling and Reasoning with Bayesian Networks
Modeling and Reasoning with Bayesian Networks

Bayesian networks have received a lot of attention over the last few decades from both scientists and engineers, and across a number of fields, including artificial intelligence (AI), statistics, cognitive science, and philosophy.

Perhaps the largest impact that Bayesian networks have had is on the field of AI, where they were...

Bayesian Argumentation: The practical side of probability (Synthese Library)
Bayesian Argumentation: The practical side of probability (Synthese Library)

Relevant to, and drawing from, a range of disciplines, the chapters in this collection show the diversity, and applicability, of research in Bayesian argumentation. Together, they form a challenge to philosophers versed in both the use and criticism of Bayesian models who have largely overlooked their potential in argumentation. Selected from...

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...
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods
Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods

This book delivers theoretical and practical knowledge for developing algorithms that infer linear and non-linear multivariate models, providing a methodology for inductive learning of polynomial neural network models (PNN) from data. The text emphasizes an organized model identification process by which to discover models that generalize and...

Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
This book presents this new discipline in a very accessible form: both as a text to train the next generation of practitioners and researchers, and to inform lifelong learners like myself. Witten and Frank have a passion for simple and elegant solutions. They approach each topic with this mindset, grounding all concepts in concrete examples,...
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