Neural Networks For Conditional Probability Estimation PDF Download
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Author | : Dirk Husmeier |
Publisher | : Springer Science & Business Media |
Total Pages | : 280 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 1447108477 |
Download Neural Networks for Conditional Probability Estimation Book in PDF, ePub and Kindle
Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the 'targets'), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and 'be nign' Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network structure for modelling conditional probability densities is derived, and it is shown that a universal approximator for this extended task requires at least two hidden layers. A training scheme is developed from a maximum likelihood approach in Chapter 3, and the performance ofthis method is demonstrated on three stochastic time series in chapters 4 and 5.
Author | : |
Publisher | : |
Total Pages | : 13 |
Release | : 2018 |
Genre | : |
ISBN | : |
Download An Alternative Model for Classification by Neural Networks Based on Bayesian Methods Book in PDF, ePub and Kindle
An alternative model to class conditional probability estimation for classification problems is introduced. The model assumes a deterministic mapping between input space and the set of classes, training data consists of probability distributions of the classes at the measurements points. A classification system is built by transforming the classification problem into regression problems, which are tackled by neural network experts using Bayesian methods. Numerical calculations on two public well-investigated real data sets are included.
Author | : Amir Sarajedini |
Publisher | : |
Total Pages | : 390 |
Release | : 1998 |
Genre | : |
ISBN | : |
Download Probability Density Estimation with Neural Networks and Its Application to Blind Signal Processing Book in PDF, ePub and Kindle
Author | : Radford M. Neal |
Publisher | : Springer Science & Business Media |
Total Pages | : 194 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461207452 |
Download Bayesian Learning for Neural Networks Book in PDF, ePub and Kindle
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.
Author | : Dirk Husmeier |
Publisher | : |
Total Pages | : |
Release | : 1998 |
Genre | : |
ISBN | : |
Download Modelling Conditional Probability Densities with Neural Networks Book in PDF, ePub and Kindle
Author | : Vassilios Petridis |
Publisher | : Springer Science & Business Media |
Total Pages | : 336 |
Release | : 1998-09-30 |
Genre | : Science |
ISBN | : 9780792382904 |
Download Predictive Modular Neural Networks Book in PDF, ePub and Kindle
The subject of this book is predictive modular neural networks and their ap plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.
Author | : Antonino Freno |
Publisher | : Springer Science & Business Media |
Total Pages | : 217 |
Release | : 2011-04-11 |
Genre | : Technology & Engineering |
ISBN | : 3642203086 |
Download Hybrid Random Fields Book in PDF, ePub and Kindle
This book presents an exciting new synthesis of directed and undirected, discrete and continuous graphical models. Combining elements of Bayesian networks and Markov random fields, the newly introduced hybrid random fields are an interesting approach to get the best of both these worlds, with an added promise of modularity and scalability. The authors have written an enjoyable book---rigorous in the treatment of the mathematical background, but also enlivened by interesting and original historical and philosophical perspectives. -- Manfred Jaeger, Aalborg Universitet The book not only marks an effective direction of investigation with significant experimental advances, but it is also---and perhaps primarily---a guide for the reader through an original trip in the space of probabilistic modeling. While digesting the book, one is enriched with a very open view of the field, with full of stimulating connections. [...] Everyone specifically interested in Bayesian networks and Markov random fields should not miss it. -- Marco Gori, Università degli Studi di Siena Graphical models are sometimes regarded---incorrectly---as an impractical approach to machine learning, assuming that they only work well for low-dimensional applications and discrete-valued domains. While guiding the reader through the major achievements of this research area in a technically detailed yet accessible way, the book is concerned with the presentation and thorough (mathematical and experimental) investigation of a novel paradigm for probabilistic graphical modeling, the hybrid random field. This model subsumes and extends both Bayesian networks and Markov random fields. Moreover, it comes with well-defined learning algorithms, both for discrete and continuous-valued domains, which fit the needs of real-world applications involving large-scale, high-dimensional data.
Author | : M.I. Jordan |
Publisher | : Springer Science & Business Media |
Total Pages | : 658 |
Release | : 2012-12-06 |
Genre | : Computers |
ISBN | : 9401150141 |
Download Learning in Graphical Models Book in PDF, ePub and Kindle
In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume. Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail. Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.
Author | : Marco Scutari |
Publisher | : CRC Press |
Total Pages | : 243 |
Release | : 2014-06-20 |
Genre | : Computers |
ISBN | : 1482225581 |
Download Bayesian Networks Book in PDF, ePub and Kindle
Understand the Foundations of Bayesian Networks—Core Properties and Definitions Explained Bayesian Networks: With Examples in R introduces Bayesian networks using a hands-on approach. Simple yet meaningful examples in R illustrate each step of the modeling process. The examples start from the simplest notions and gradually increase in complexity. The authors also distinguish the probabilistic models from their estimation with data sets. The first three chapters explain the whole process of Bayesian network modeling, from structure learning to parameter learning to inference. These chapters cover discrete Bayesian, Gaussian Bayesian, and hybrid networks, including arbitrary random variables. The book then gives a concise but rigorous treatment of the fundamentals of Bayesian networks and offers an introduction to causal Bayesian networks. It also presents an overview of R and other software packages appropriate for Bayesian networks. The final chapter evaluates two real-world examples: a landmark causal protein signaling network paper and graphical modeling approaches for predicting the composition of different body parts. Suitable for graduate students and non-statisticians, this text provides an introductory overview of Bayesian networks. It gives readers a clear, practical understanding of the general approach and steps involved.
Author | : Ming-Lei Tseng |
Publisher | : |
Total Pages | : 426 |
Release | : 1991 |
Genre | : |
ISBN | : |
Download Integrating Neural Networks with Influence Diagrams for Multiple Sensor Diagnostic Systems Book in PDF, ePub and Kindle