The problem of low-rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a Wishart distribution is specified as a hyperprior over the precision matrix. W...

We investigate the Student-t process as an alternative to the Gaussian process as a nonparametric prior over functions. We derive closed form expressions for the marginal likelihood and predictive distribution of a Student-t process, by integrating away an inverse Wishart process prior over the covariance kernel of a Gaussian process model. We show surprising equivalences between different hier...

Abstract We study the inverse problem of estimating a field u from data comprising a finite set of nonlinear functionals of u, subject to additive noise; we denote this observed data by y. Our interest is in the reconstruction of piecewise continuous fields u in which the discontinuity set is described by a finite number of geometric parameters a. Natural applications include groundwater flow a...

a two-factor experiment with interaction between factors wherein observations follow an inverse gaussian model is considered. analysis of the experiment is approached via an empirical bayes procedure. the conjugate family of prior distributions is considered. bayes and empirical bayes estimators are derived. application of the procedure is illustrated on a data set, which has previously been an...

We propose a scaled stochastic Newton algorithm (sSN) for local Metropolis-Hastings Markov chain Monte Carlo (MCMC) simulations. The method can be considered as an EulerMaruyama discretization of the Langevin diffusion on a Riemann manifold with piecewise constant Hessian of the negative logarithm of the target density as the metric tensor. The sSN proposal consists of deterministic and stochas...

The application of bootstrap methods to regression models helps approximate the distribution of the coefficients and the distribution of the prediction errors. In this paper, we are concerned with the application of the bootstrap techniques to determine prediction intervals on econometric models when the regressors are known. We investigate problems associated with its application: determinatio...

the video quality assessment must be adapted to the human visual system, which is why researchers have performed subjective viewing experiments in order to obtain the conditions of encoding of video systems to provide the best quality to the user. the objective of this study is to assess the video quality using image features extraction without using reference video. rmse values and processing ...

Income distribution models can be useful for describing the economic properties of a population. In this study, three-part composite Pareto are fitted to income in Malaysia years 2007, 2009, 2012, 2014, and 2016. The divide population into three parts, each following different model. lower part follows inverse distribution, upper middle another unspecified For application data, use Gaussian mix...