Get 40% off of select print and eBooks in Engineering & Materials Science!

Perspectives in Neural Computing

Neural Networks for Conditional Probability Estimation

Forecasting Beyond Point Predictions

Authors: Husmeier, Dirk

Free Preview

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-1-4471-0847-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.99
price for USA in USD
  • ISBN 978-1-85233-095-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
About this book

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 GausĀ­ 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 'beĀ­ 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.

Table of contents (18 chapters)

Table of contents (18 chapters)

Buy this book

eBook $84.99
price for USA in USD (gross)
  • ISBN 978-1-4471-0847-4
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Softcover $109.99
price for USA in USD
  • ISBN 978-1-85233-095-8
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Loading...

Recommended for you

Loading...

Bibliographic Information

Bibliographic Information
Book Title
Neural Networks for Conditional Probability Estimation
Book Subtitle
Forecasting Beyond Point Predictions
Authors
Series Title
Perspectives in Neural Computing
Copyright
1999
Publisher
Springer-Verlag London
Copyright Holder
Springer-Verlag London Limited
eBook ISBN
978-1-4471-0847-4
DOI
10.1007/978-1-4471-0847-4
Softcover ISBN
978-1-85233-095-8
Series ISSN
1431-6854
Edition Number
1
Number of Pages
XXIII, 275
Number of Illustrations
13 b/w illustrations
Topics