One of the most common problems encountered in agriculture is that of predicting a response variable from covariates of interest. The aim of this paper is to use a Bayesian neural network approach to predict dairy daughter milk production from dairy dam, sire, herd and environmental factors. The results of the Bayesian neural network are compared with the results obtained when the regression re...

Abstract We propose a novel approach to calibrate the conditional value-at-risk (CoVaR) of financial institutions based on neural network quantile regression. Building estimation results, we model systemic risk spillover effects in context across banks by considering marginal regression procedure. An out-of-sample analysis shows great performance compared linear baseline specification, signifyi...

In this contribution we present a method for solving the inverse problem in electric impedance tomography with Bayesian MLP neural network. The problem of reconstructing the conductivity distribution inside an object from potential measurements from the surface is known to be ill-posed, requiring efficient regularization techniques. We decompose the reconstruction problem to lower dimensional p...

To formalise our discussion of model uncertainty we will rely on probabilistic modelling, and more specifically on Bayesian modelling. Bayesian probability theory offers us the machinery we need to develop our tools. Together with techniques for approximate inference in Bayesian models, in the next chapter we will present the main results of this work. But prior to that, let us review the main ...

It is difficult to accurately reckon vehicle position for vehicle navigation system (VNS) during GPS outages, a novel prediction algorithm of dead reckon (DR) position error is put forward, which based on Bayesian regularization back-propagation (BRBP) neural network. DR, GPS position data are first denoised and compared at different stationary wavelet transformation (SWT) decomposition level, ...

Neural networks are parameterized non-linear models used for empirical regression and classi-cation modelling. Their exibility makes them able to discover more general relationships in data than traditional statistical models. Bayesian probability theory provides a unifying framework for data modeling which ooers several beneets. First, the overrtting problem can be solved by using Bayesian met...

The generalization ability of a neural network can sometimes be improved dramatically by regularization. To analyze the improvement one needs more refined results than the asymptotic distribution of the weight vector. Here we study the simple case of one-dimensional linear regression under quadratic regularization, i.e., ridge regression. We study the random design, misspecified case, where we ...

ABSTRACT: In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water...