Distributional neural networks for electricity price forecasting
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 …
distributional neural networks. The model structure is based on a deep neural network …
Fast ε-free inference of simulation models with bayesian conditional density estimation
G Papamakarios, I Murray - Advances in neural information …, 2016 - proceedings.neurips.cc
Many statistical models can be simulated forwards but have intractable likelihoods.
Approximate Bayesian Computation (ABC) methods are used to infer properties of these …
Approximate Bayesian Computation (ABC) methods are used to infer properties of these …
Most likely heteroscedastic Gaussian process regression
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 …
dependent noise rates. We follow Goldberg et al.'s approach and model the noise variance …
[图书][B] Machine learning for spatial environmental data: theory, applications, and software
M Kanevski, V Timonin, A Pozdnukhov - 2009 - taylorfrancis.com
This book discusses machine learning algorithms, such as artificial neural networks of
different architectures, statistical learning theory, and Support Vector Machines used for the …
different architectures, statistical learning theory, and Support Vector Machines used for the …
Predictive uncertainty in environmental modelling
Artificial neural networks have proved an attractive approach to non-linear regression
problems arising in environmental modelling, such as statistical downscaling, short-term …
problems arising in environmental modelling, such as statistical downscaling, short-term …
[图书][B] Analysis and modelling of spatial environmental data
M Kanevski, M Maignan - 2004 - books.google.com
Analysis and Modelling of Spatial Environmental Data presents traditional geostatistics
methods for variography and spatial predictions, approaches to conditional stochastic …
methods for variography and spatial predictions, approaches to conditional stochastic …
Bayesian geophysical inversion using invertible neural networks
Constraining geophysical models with observed data usually involves solving nonlinear and
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …
nonunique inverse problems. Neural mixture density networks (MDNs) provide an efficient …
A rigorous inter-comparison of ground-level ozone predictions
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 …
several scientific fields but have not, until now, been comprehensively evaluated for …
Information science and statistics
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 …
Kleinberg B. Schölkopf Page 3 Information Science and Statistics For other titles published in …
Deep inference for covariance estimation: Learning gaussian noise models for state estimation
We present a novel method of measurement covariance estimation that models
measurement uncertainty as a function of the measurement itself. Existing work in predictive …
measurement uncertainty as a function of the measurement itself. Existing work in predictive …