Dirk Husmeier's Books

Neural Networks for Conditional Probability Estimation

Forecasting Beyond Point Predictions

By: Dirk Husmeier

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Neural Networks for Conditional Probability Estimation

Forecasting Beyond Point Predictions

By: Dirk Husmeier

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.

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Page Count

280

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1447108477

ISBN 13

9781447108474

Probabilistic Modeling in Bioinformatics and Medical Informatics

By: Dirk Husmeier,Richard Dybowski

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Probabilistic Modeling in Bioinformatics and Medical Informatics

By: Dirk Husmeier,Richard Dybowski

Written for researchers and students in statistics, machine learning, and the biological sciences. This book provides a self-contained introduction to the methodology of Bayesian networks. It offers both elementary tutorials as well as more advanced applications and case studies.

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Page Count

540

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1852337788

ISBN 13

9781852337780

Probabilistic Modeling in Bioinformatics and Medical Informatics

By: Richard Dybowski,Dirk Husmeier,Stephen Roberts

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Probabilistic Modeling in Bioinformatics and Medical Informatics

By: Richard Dybowski,Dirk Husmeier,Stephen Roberts

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.

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504

Published Date

ISBN 10

6610346658

ISBN 13

9786610346653

Neural Networks for Conditional Probability Estimation

Forecasting Beyond Point Predictions

By: Dirk Husmeier

Write a review Read reviews Take a Quiz Solve Book Puzzle

Neural Networks for Conditional Probability Estimation

Forecasting Beyond Point Predictions

By: Dirk Husmeier

Average ratings

N/A

Write a review

Solve jigsaw puzzle

Age

Page Count

280

Publisher

Springer Science & Business Media

Published Date

ISBN 10

1447108477

ISBN 13

9781447108474

Book's by Dirk Husmeier

Neural Networks for Conditional Probability Estimation

Neural Networks for Conditional Probability Estimation

Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modeling in Bioinformatics and Medical Informatics

Probabilistic Modeling in Bioinformatics and Medical Informatics

Neural Networks for Conditional Probability Estimation

Neural Networks for Conditional Probability Estimation

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