Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning
Author: David Barber
Publsiher: Cambridge University Press
Total Pages: 739
Release: 2012-02-02
Genre: Computers
ISBN: 9780521518147

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A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications

Bayesian Reasoning and Gaussian Processes for Machine Learning Applications
Author: Hemachandran K,Shubham Tayal,Preetha Mary George,Parveen Singla,Utku Kose
Publsiher: CRC Press
Total Pages: 147
Release: 2022-04-14
Genre: Business & Economics
ISBN: 9781000569582

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This book introduces Bayesian reasoning and Gaussian processes into machine learning applications. Bayesian methods are applied in many areas, such as game development, decision making, and drug discovery. It is very effective for machine learning algorithms in handling missing data and extracting information from small datasets. Bayesian Reasoning and Gaussian Processes for Machine Learning Applications uses a statistical background to understand continuous distributions and how learning can be viewed from a probabilistic framework. The chapters progress into such machine learning topics as belief network and Bayesian reinforcement learning, which is followed by Gaussian process introduction, classification, regression, covariance, and performance analysis of Gaussian processes with other models. FEATURES Contains recent advancements in machine learning Highlights applications of machine learning algorithms Offers both quantitative and qualitative research Includes numerous case studies This book is aimed at graduates, researchers, and professionals in the field of data science and machine learning.

Modeling and Reasoning with Bayesian Networks

Modeling and Reasoning with Bayesian Networks
Author: Adnan Darwiche
Publsiher: Cambridge University Press
Total Pages: 561
Release: 2009-04-06
Genre: Computers
ISBN: 9780521884389

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This book provides a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Bayesian Reinforcement Learning

Bayesian Reinforcement Learning
Author: Mohammad Ghavamzadeh,Shie Mannor,Joelle Pineau,Aviv Tamar
Publsiher: Unknown
Total Pages: 146
Release: 2015-11-18
Genre: Computers
ISBN: 1680830880

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Bayesian methods for machine learning have been widely investigated, yielding principled methods for incorporating prior information into inference algorithms. This monograph provides the reader with an in-depth review of the role of Bayesian methods for the reinforcement learning (RL) paradigm. The major incentives for incorporating Bayesian reasoning in RL are that it provides an elegant approach to action-selection (exploration/exploitation) as a function of the uncertainty in learning, and it provides a machinery to incorporate prior knowledge into the algorithms. Bayesian Reinforcement Learning: A Survey first discusses models and methods for Bayesian inference in the simple single-step Bandit model. It then reviews the extensive recent literature on Bayesian methods for model-based RL, where prior information can be expressed on the parameters of the Markov model. It also presents Bayesian methods for model-free RL, where priors are expressed over the value function or policy class. Bayesian Reinforcement Learning: A Survey is a comprehensive reference for students and researchers with an interest in Bayesian RL algorithms and their theoretical and empirical properties.

Bayesian Time Series Models

Bayesian Time Series Models
Author: David Barber,A. Taylan Cemgil,Silvia Chiappa
Publsiher: Cambridge University Press
Total Pages: 432
Release: 2011-08-11
Genre: Computers
ISBN: 9780521196765

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The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Bayesian Learning for Neural Networks

Bayesian Learning for Neural Networks
Author: Radford M. Neal
Publsiher: Springer Science & Business Media
Total Pages: 194
Release: 2012-12-06
Genre: Mathematics
ISBN: 9781461207450

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Artificial "neural networks" are widely used as flexible models for classification and regression applications, but questions remain about how the power of these models can be safely exploited when training data is limited. This book demonstrates how Bayesian methods allow complex neural network models to be used without fear of the "overfitting" that can occur with traditional training methods. Insight into the nature of these complex Bayesian models is provided by a theoretical investigation of the priors over functions that underlie them. A practical implementation of Bayesian neural network learning using Markov chain Monte Carlo methods is also described, and software for it is freely available over the Internet. Presupposing only basic knowledge of probability and statistics, this book should be of interest to researchers in statistics, engineering, and artificial intelligence.

Bayesian Programming

Bayesian Programming
Author: Pierre Bessiere,Emmanuel Mazer,Juan Ahuactzin,Kamel Mekhnacha
Publsiher: CRC Press
Total Pages: 380
Release: 2013-12-20
Genre: Business & Economics
ISBN: 9781439880333

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Probability as an Alternative to Boolean LogicWhile logic is the mathematical foundation of rational reasoning and the fundamental principle of computing, it is restricted to problems where information is both complete and certain. However, many real-world problems, from financial investments to email filtering, are incomplete or uncertain in natur

Probabilistic Machine Learning

Probabilistic Machine Learning
Author: Kevin P. Murphy
Publsiher: MIT Press
Total Pages: 858
Release: 2022-03-01
Genre: Computers
ISBN: 9780262369305

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A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the new book is accompanied by online Python code, using libraries such as scikit-learn, JAX, PyTorch, and Tensorflow, which can be used to reproduce nearly all the figures; this code can be run inside a web browser using cloud-based notebooks, and provides a practical complement to the theoretical topics discussed in the book. This introductory text will be followed by a sequel that covers more advanced topics, taking the same probabilistic approach.