Machine learning using approximate inference

Machine learning using approximate inference PDF

Author: Christian Andersson Naesseth

Publisher: Linköping University Electronic Press

Published: 2018-11-27

Total Pages: 39

ISBN-13: 9176851613

DOWNLOAD EBOOK →

Automatic decision making and pattern recognition under uncertainty are difficult tasks that are ubiquitous in our everyday life. The systems we design, and technology we develop, requires us to coherently represent and work with uncertainty in data. Probabilistic models and probabilistic inference gives us a powerful framework for solving this problem. Using this framework, while enticing, results in difficult-to-compute integrals and probabilities when conditioning on the observed data. This means we have a need for approximate inference, methods that solves the problem approximately using a systematic approach. In this thesis we develop new methods for efficient approximate inference in probabilistic models. There are generally two approaches to approximate inference, variational methods and Monte Carlo methods. In Monte Carlo methods we use a large number of random samples to approximate the integral of interest. With variational methods, on the other hand, we turn the integration problem into that of an optimization problem. We develop algorithms of both types and bridge the gap between them. First, we present a self-contained tutorial to the popular sequential Monte Carlo (SMC) class of methods. Next, we propose new algorithms and applications based on SMC for approximate inference in probabilistic graphical models. We derive nested sequential Monte Carlo, a new algorithm particularly well suited for inference in a large class of high-dimensional probabilistic models. Then, inspired by similar ideas we derive interacting particle Markov chain Monte Carlo to make use of parallelization to speed up approximate inference for universal probabilistic programming languages. After that, we show how we can make use of the rejection sampling process when generating gamma distributed random variables to speed up variational inference. Finally, we bridge the gap between SMC and variational methods by developing variational sequential Monte Carlo, a new flexible family of variational approximations.

Advanced Lectures on Machine Learning

Advanced Lectures on Machine Learning PDF

Author: Olivier Bousquet

Publisher: Springer

Published: 2011-03-22

Total Pages: 249

ISBN-13: 3540286500

DOWNLOAD EBOOK →

Machine Learning has become a key enabling technology for many engineering applications, investigating scientific questions and theoretical problems alike. To stimulate discussions and to disseminate new results, a summer school series was started in February 2002, the documentation of which is published as LNAI 2600. This book presents revised lectures of two subsequent summer schools held in 2003 in Canberra, Australia, and in Tübingen, Germany. The tutorial lectures included are devoted to statistical learning theory, unsupervised learning, Bayesian inference, and applications in pattern recognition; they provide in-depth overviews of exciting new developments and contain a large number of references. Graduate students, lecturers, researchers and professionals alike will find this book a useful resource in learning and teaching machine learning.

Information Theory, Inference and Learning Algorithms

Information Theory, Inference and Learning Algorithms PDF

Author: David J. C. MacKay

Publisher: Cambridge University Press

Published: 2003-09-25

Total Pages: 694

ISBN-13: 9780521642989

DOWNLOAD EBOOK →

Information theory and inference, taught together in this exciting textbook, lie at the heart of many important areas of modern technology - communication, signal processing, data mining, machine learning, pattern recognition, computational neuroscience, bioinformatics and cryptography. The book introduces theory in tandem with applications. Information theory is taught alongside practical communication systems such as arithmetic coding for data compression and sparse-graph codes for error-correction. Inference techniques, including message-passing algorithms, Monte Carlo methods and variational approximations, are developed alongside applications to clustering, convolutional codes, independent component analysis, and neural networks. Uniquely, the book covers state-of-the-art error-correcting codes, including low-density-parity-check codes, turbo codes, and digital fountain codes - the twenty-first-century standards for satellite communications, disk drives, and data broadcast. Richly illustrated, filled with worked examples and over 400 exercises, some with detailed solutions, the book is ideal for self-learning, and for undergraduate or graduate courses. It also provides an unparalleled entry point for professionals in areas as diverse as computational biology, financial engineering and machine learning.

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields

Approximate Inference, Structure Learning and Feature Estimation in Markov Random Fields PDF

Author: Pradeep Ravikumar

Publisher:

Published: 2007

Total Pages: 139

ISBN-13:

DOWNLOAD EBOOK →

Abstract: "Markov random fields (MRFs), or undirected graphical models, are graphical representations of probability distributions. Each graph represents a family of distributions -- the nodes of the graph represent random variables, the edges encode independence assumptions, and weights over the edges and cliques specify a particular member of the family. There are three main classes of tasks within this framework: the first is to perform inference, given the graph structure and parameters and (clique) feature functions; the second is to estimate the graph structure and parameters from data, given the feature functions; the third is to estimate the feature functions themselves from data. Key inference subtasks include estimating the normalization constant (also called the partition function), event probability estimation, computing rigorous upper and lower bounds (interval guarantees), inference given only moment constraints, and computing the most probable configuration. The thesis addresses all of the above tasks and subtasks."

An Introduction to Lifted Probabilistic Inference

An Introduction to Lifted Probabilistic Inference PDF

Author: Guy Van den Broeck

Publisher: MIT Press

Published: 2021-08-17

Total Pages: 455

ISBN-13: 0262542595

DOWNLOAD EBOOK →

Recent advances in the area of lifted inference, which exploits the structure inherent in relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of reasoning under uncertainty with reasoning about individuals and relations. The representations used are often called relational probabilistic models. Lifted inference is about how to exploit the structure inherent in relational probabilistic models, either in the way they are expressed or by extracting structure from observations. This book covers recent significant advances in the area of lifted inference, providing a unifying introduction to this very active field. After providing necessary background on probabilistic graphical models, relational probabilistic models, and learning inside these models, the book turns to lifted inference, first covering exact inference and then approximate inference. In addition, the book considers the theory of liftability and acting in relational domains, which allows the connection of learning and reasoning in relational domains.

Model-Based Machine Learning

Model-Based Machine Learning PDF

Author: John Winn

Publisher: CRC Press

Published: 2023-11-30

Total Pages: 469

ISBN-13: 1498756824

DOWNLOAD EBOOK →

Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems. Explains machine learning concepts as they arise in real-world case studies. Shows how to diagnose, understand and address problems with machine learning systems. Full source code available, allowing models and results to be reproduced and explored. Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader.

Bayesian Reasoning and Machine Learning

Bayesian Reasoning and Machine Learning PDF

Author: David Barber

Publisher: Cambridge University Press

Published: 2012-02-02

Total Pages: 739

ISBN-13: 0521518148

DOWNLOAD EBOOK →

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

Lanterna Rally - Theme by Grace Themes