We present a novel approach to probabilistic electricity price forecasting which utilizes
distributional neural networks. The model structure is based on a deep neural network …

Many statistical models can be simulated forwards but have intractable likelihoods.
Approximate Bayesian Computation (ABC) methods are used to infer properties of these …

This paper presents a novel Gaussian process (GP) approach to regression with input-
dependent noise rates. We follow Goldberg et al.'s approach and model the noise variance …

This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …

Artificial neural networks have proved an attractive approach to non-linear regression
problems arising in environmental modelling, such as statistical downscaling, short-term …

Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics
methods for variography and spatial predictions, approaches to conditional stochastic …

Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …

Novel statistical approaches to prediction have recently been shown to perform well in
several scientific fields but have not, until now, been comprehensively evaluated for …

Untitled Page 1 Page 2 Information Science and Statistics Series Editors: M. Jordan J.
Kleinberg B. Schölkopf Page 3 Information Science and Statistics For other titles published in …

We present a novel method of measurement covariance estimation that models
measurement uncertainty as a function of the measurement itself. Existing work in predictive …