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Bayesian Nonlinear Modelling for the Prediction Competition
 In ASHRAE Transactions, V.100, Pt.2
, 1994
"... . The 1993 energy prediction competition involved the prediction of a series of building energy loads from a series of environmental input variables. Nonlinear regression using `neural networks' is a popular technique for such modeling tasks. Since it is not obvious how large a timewindow of inpu ..."
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Cited by 51 (4 self)
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. The 1993 energy prediction competition involved the prediction of a series of building energy loads from a series of environmental input variables. Nonlinear regression using `neural networks' is a popular technique for such modeling tasks. Since it is not obvious how large a timewindow of inputs is appropriate, or what preprocessing of inputs is best, this can be viewed as a regression problem in which there are many possible input variables, some of which may actually be irrelevant to the prediction of the output variable. Because a finite data set will show random correlations between the irrelevant inputs and the output, any conventional neural network (even with regularisation or `weight decay') will not set the coefficients for these junk inputs to zero. Thus the irrelevant variables will hurt the model's performance. The Automatic Relevance Determination (ARD) model puts a prior over the regression parameters which embodies the concept of relevance. This is done in a simple...
Bayesian Neural Networks and Density Networks
 Nuclear Instruments and Methods in Physics Research, A
, 1994
"... This paper reviews the Bayesian approach to learning in neural networks, then introduces a new adaptive model, the density network. This is a neural network for which target outputs are provided, but the inputs are unspecied. When a probability distribution is placed on the unknown inputs, a latent ..."
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Cited by 40 (8 self)
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This paper reviews the Bayesian approach to learning in neural networks, then introduces a new adaptive model, the density network. This is a neural network for which target outputs are provided, but the inputs are unspecied. When a probability distribution is placed on the unknown inputs, a latent variable model is dened that is capable of discovering the underlying dimensionality of a data set. A Bayesian learning algorithm for these networks is derived and demonstrated. 1 Introduction to the Bayesian view of learning A binary classier is a parameterized mapping from an input x to an output y 2 [0; 1]); when its parameters w are specied, the classier states the probability that an input x belongs to class t = 1, rather than the alternative t = 0. Consider a binary classier which models the probability as a sigmoid function of x: P (t = 1jx; w;H) = y(x; w;H) = 1 1 + e wx (1) This form of model is known to statisticians as a linear logistic model, and in the neural networks ...
Bayesian Methods for Neural Networks: Theory and Applications
, 1995
"... this document. Before these are discussed however, perhaps we should have a tutorial on Bayesian probability theory and its application to model comparison problems. 2 Probability theory and Occam's razor ..."
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Cited by 13 (0 self)
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this document. Before these are discussed however, perhaps we should have a tutorial on Bayesian probability theory and its application to model comparison problems. 2 Probability theory and Occam's razor
Density Networks and their Application to Protein Modelling
 In Maximum Entropy and Bayesian Methods
, 1996
"... . I define a latent variable model in the form of a neural network for which only target outputs are specified; the inputs are unspecified. Although the inputs are missing, it is still possible to train this model by placing a simple probability distribution on the unknown inputs and maximizing the ..."
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Cited by 7 (3 self)
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. I define a latent variable model in the form of a neural network for which only target outputs are specified; the inputs are unspecified. Although the inputs are missing, it is still possible to train this model by placing a simple probability distribution on the unknown inputs and maximizing the probability of the data given the parameters. The model can then discover for itself a description of the data in terms of an underlying latent variable space of lower dimensionality. I present preliminary results of the application of these models to protein data. 1 Density Modelling The most popular supervised neural networks, multilayer perceptrons (MLPs), are well established as probabilistic models for regression and classification, both of which are conditional modelling tasks: the input variables are assumed given, and we condition on their values when modelling the distribution over the output variables; no model of the density over input variables is constructed. Density modelling...
Bayesian Regularisation Methods In A Hybrid MlpHmm System
 in â€˜Proceedings of the European Conference on Speech Technology
, 1993
"... We have applied Bayesian regularisation methods to multilayer perceptron (MLP) training in the context of a hybrid MLP HMM (hidden Markov model) continuous speech recognition system. The Bayesian framework adopted here allows an objective setting of the regularisation parameters, according to the ..."
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Cited by 1 (0 self)
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We have applied Bayesian regularisation methods to multilayer perceptron (MLP) training in the context of a hybrid MLP HMM (hidden Markov model) continuous speech recognition system. The Bayesian framework adopted here allows an objective setting of the regularisation parameters, according to the training data. Experiments were carried out on the ARPA Resource Management database.
Gaussian Process Regression Networks
, 1110
"... We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple ..."
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We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the nonparametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent lengthscales and amplitudes, and heavytailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multitask) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset. 1