Forthcoming astronomical surveys are expected to detect new sources in such large numbers that measuring their spectroscopic redshift measurements will be not practical. Thus, there is much interest using machine learning yield the from photometry of each object. We particularly interested radio (quasars) detected with Square Kilometre Array and have found Deep Learning, trained upon a opticall...

We present our submission to the Extreme Value Analysis 2021 Data Challenge in which teams were asked accurately predict distributions of wildfire frequency and size within spatio-temporal regions missing data. For this competition, we developed a variant powerful variational autoencoder models, call Conditional Missing data Importance-Weighted Autoencoder (CMIWAE). Our deep latent variable gen...

in this investigation 13699 data related to milk production from 2716 holstein first calving and daughter from 167 sire cows were analyzed based on random regression model (rr/cf) and reml method incomplete lactating (5-90 days) and (5-120 days) in correlations to complete lactating were 0.67 and 0.72, respectively with progress in complete 5-240 day was 0.91 the correlation value for lactating...

Relational data are usually highly incomplete in practice, which inspires us to leverage side information to improve the performance of community detection and link prediction. This paper presents a Bayesian probabilistic approach that incorporates various kinds of node attributes encoded in binary form in relational models with Poisson likelihood. Our method works flexibly with both directed a...

Classification is an important research topic in knowledge discovery. Most of the researches on classification concern that a complete dataset is given as a training dataset and the test data contain all values of attributes without missing. Unfortunately, incomplete data usually exist in real-world applications. In this paper, we propose new handling schemes of learning classification models f...

We propose two ways of estimating current source density (CSD) from measurements of voltage on a Cartesian grid with missing recording points using the inverse CSD method. The simplest approach is to substitute local averages (LA) in place of missing data. A more elaborate alternative is to estimate a smaller number of CSD parameters than the actual number of recordings and to take the least-sq...

The two-parameter exponential distribution can often be used to describe the lifetime of products for example, electronic components, engines and so on. This paper considers a prediction problem arising in the life test of key parts in high speed trains. Employing the Bayes method, a joint prior is used to describe the variability of the parameters but the form of the prior is not specified and...

The problem of incomplete data—i.e., data with missing or unknown values—in multi-way arrays is ubiquitous in biomedical signal processing, network traffic analysis, bibliometrics, social network analysis, chemometrics, computer vision, communication networks, etc. We consider the problem of how to factorize data sets with missing values with the goal of capturing the underlying latent structur...

We construct genRBF kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data into the function space and to define a kernel between two missing data points based...