Author: Charles GaddPublisher:
ISBN:
Category : Alzheimer's disease
Languages : en
Pages : 334
Book Description
Probabilistic Modelling of Uncertainty with Bayesian Nonparametric Machine Learning
Author: Charles GaddPublisher:
ISBN:
Category : Alzheimer's disease
Languages : en
Pages : 334
Book Description
Bayesian Nonparametric Probabilistic Methods in Machine Learning
Author: Justin C. SahsPublisher:
ISBN:
Category : Artificial intelligence
Languages : en
Pages :
Book Description
Many aspects of modern science, business and engineering have become data-centric, relying on tools from Artificial Intelligence and Machine Learning. Practitioners and researchers in these fields need tools that can incorporate observed data into rich models of uncertainty to make discoveries and predictions. One area of study that provides such models is the field of Bayesian Nonparametrics. This dissertation is focused on furthering the development of this field. After reviewing the relevant background and surveying the field, we consider two areas of structured data: - We first consider relational data that takes the form of a 2-dimensional array--such as social network data. We introduce a novel nonparametric model that takes advantage of a representation theorem about arrays whose column and row order is unimportant. We then develop an inference algorithm for this model and evaluate it experimentally. - Second, we consider the classification of streaming data whose distribution evolves over time. We introduce a novel nonparametric model that finds and exploits a dynamic hierarchical structure underlying the data. We present an algorithm for inference in this model and show experimental results. We then extend our streaming model to handle the emergence of novel and recurrent classes, and evaluate the extended model experimentally.
Bayesian Nonparametrics via Neural Networks
Author: Herbert K. H. LeePublisher: SIAM
ISBN: 9780898718423
Category : Mathematics
Languages : en
Pages : 106
Book Description
Bayesian Nonparametrics via Neural Networks is the first book to focus on neural networks in the context of nonparametric regression and classification, working within the Bayesian paradigm. Its goal is to demystify neural networks, putting them firmly in a statistical context rather than treating them as a black box. This approach is in contrast to existing books, which tend to treat neural networks as a machine learning algorithm instead of a statistical model. Once this underlying statistical model is recognized, other standard statistical techniques can be applied to improve the model. The Bayesian approach allows better accounting for uncertainty. This book covers uncertainty in model choice and methods to deal with this issue, exploring a number of ideas from statistics and machine learning. A detailed discussion on the choice of prior and new noninformative priors is included, along with a substantial literature review. Written for statisticians using statistical terminology, Bayesian Nonparametrics via Neural Networks will lead statisticians to an increased understanding of the neural network model and its applicability to real-world problems.
Handbook of Probabilistic Models
Author: Pijush SamuiPublisher: Butterworth-Heinemann
ISBN: 0128165464
Category : Computers
Languages : en
Pages : 592
Book Description
Handbook of Probabilistic Models carefully examines the application of advanced probabilistic models in conventional engineering fields. In this comprehensive handbook, practitioners, researchers and scientists will find detailed explanations of technical concepts, applications of the proposed methods, and the respective scientific approaches needed to solve the problem. This book provides an interdisciplinary approach that creates advanced probabilistic models for engineering fields, ranging from conventional fields of mechanical engineering and civil engineering, to electronics, electrical, earth sciences, climate, agriculture, water resource, mathematical sciences and computer sciences. Specific topics covered include minimax probability machine regression, stochastic finite element method, relevance vector machine, logistic regression, Monte Carlo simulations, random matrix, Gaussian process regression, Kalman filter, stochastic optimization, maximum likelihood, Bayesian inference, Bayesian update, kriging, copula-statistical models, and more. - Explains the application of advanced probabilistic models encompassing multidisciplinary research - Applies probabilistic modeling to emerging areas in engineering - Provides an interdisciplinary approach to probabilistic models and their applications, thus solving a wide range of practical problems
Bayesian Reasoning and Machine Learning
Author: David BarberPublisher: Cambridge University Press
ISBN: 0521518148
Category : Computers
Languages : en
Pages : 739
Book Description
A practical introduction perfect for final-year undergraduate and graduate students without a solid background in linear algebra and calculus.
Bayesian Networks and Decision Graphs
Author: Thomas Dyhre NielsenPublisher: Springer Science & Business Media
ISBN: 0387682813
Category : Science
Languages : en
Pages : 457
Book Description
This is a brand new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision process and partially ordered decision problems.
Bayesian Nonparametrics
Author: Nils Lid HjortPublisher: Cambridge University Press
ISBN: 1139484605
Category : Mathematics
Languages : en
Pages : 309
Book Description
Bayesian nonparametrics works - theoretically, computationally. The theory provides highly flexible models whose complexity grows appropriately with the amount of data. Computational issues, though challenging, are no longer intractable. All that is needed is an entry point: this intelligent book is the perfect guide to what can seem a forbidding landscape. Tutorial chapters by Ghosal, Lijoi and Prünster, Teh and Jordan, and Dunson advance from theory, to basic models and hierarchical modeling, to applications and implementation, particularly in computer science and biostatistics. These are complemented by companion chapters by the editors and Griffin and Quintana, providing additional models, examining computational issues, identifying future growth areas, and giving links to related topics. This coherent text gives ready access both to underlying principles and to state-of-the-art practice. Specific examples are drawn from information retrieval, NLP, machine vision, computational biology, biostatistics, and bioinformatics.
Planning Under Uncertainty with Bayesian Nonparametric Models
Author: Robert Henry KleinPublisher:
ISBN:
Category :
Languages : en
Pages : 119
Book Description
Autonomous agents are increasingly being called upon to perform challenging tasks in complex settings with little information about underlying environment dynamics. To successfully complete such tasks the agent must learn from its interactions with the environment. Many existing techniques make assumptions about problem structure to remain tractable, such as limiting the class of possible models or specifying a fixed model expressive power. Complicating matters, there are many scenarios where the environment exhibits multiple underlying sets of dynamics; in these cases, most existing approaches assume the number of underlying models is known a priori, or ignore the possibility of multiple models altogether. Bayesian nonparametric (BNP) methods provide the flexibility to solve both of these problems, but have high inference complexity that has limited their adoption. This thesis provides several methods to tractably plan under uncertainty using BNPs. The first is Simultaneous Clustering on Representation Expansion (SCORE) for learning Markov Decision Processes (MDPs) that exhibit an underlying multiple-model structure. SCORE addresses the co-dependence between observation clustering and model expansion. The second contribution provides a realtime, non-myopic, risk-aware planning solution for use in camera surveillance scenarios where the number of underlying target behaviors and their parameterization are unknown. A BNP model is used to capture target behaviors, and a solution that reduces uncertainty only as needed to perform a mission is presented for allocating cameras. The final contribution is a reinforcement learning (RL) framework RLPy, a software package to promote collaboration and speed innovation in the RL community. RLPy provides a library of learning agents, function approximators, and problem domains for performing RL experiments. RLPy also provides a suite of tools that help automate tasks throughout the experiment pipeline, from initial prototyping through hyperparameter optimization, parallelization of large-scale experiments, and final publication-ready plotting.
Bayesian Methods for Nonlinear Classification and Regression
Author: David G. T. DenisonPublisher: John Wiley & Sons
ISBN: 9780471490364
Category : Mathematics
Languages : en
Pages : 302
Book Description
Bei der Regressionsanalyse von Datenmaterial erhält man leider selten lineare oder andere einfache Zusammenhänge (parametrische Modelle). Dieses Buch hilft Ihnen, auch komplexere, nichtparametrische Modelle zu verstehen und zu beherrschen. Stärken und Schwächen jedes einzelnen Modells werden durch die Anwendung auf Standarddatensätze demonstriert. Verbreitete nichtparametrische Modelle werden mit Hilfe von Bayes-Verfahren in einen kohärenten wahrscheinlichkeitstheoretischen Zusammenhang gebracht.
Probabilistic Networks and Expert Systems
Author: Robert G. CowellPublisher: Springer Science & Business Media
ISBN: 9780387718231
Category : Computers
Languages : en
Pages : 340
Book Description
Probabilistic expert systems are graphical networks which support the modeling of uncertainty and decisions in large complex domains, while retaining ease of calculation. Building on original research by the authors, this book gives a thorough and rigorous mathematical treatment of the underlying ideas, structures, and algorithms. The book will be of interest to researchers in both artificial intelligence and statistics, who desire an introduction to this fascinating and rapidly developing field. The book, winner of the DeGroot Prize 2002, the only book prize in the field of statistics, is new in paperback.