Accurate prediction of future onset of disease from Electronic Health Records (EHRs) has important clinical and economic implications. In this domain the arrival of data comes at semi-irregular intervals and makes the prediction task challenging. We propose a method called multiplicative-forest point processes (MFPPs) that learns the rate of future events based on an event history. MFPPs join p...

In this paper, the problem of estimation parameters and prediction order statistics from a future sample based on upper k-record values arising two parameter Weibull distribution is considered. The maximum likelihood estimators Bayes are obtained observed values. Bayesian point predictors its two-sided equi-tailed intervals also derived. Finally, numerical illustration considered for exemplifyi...

We propose a new method for conducting Bayesian prediction that delivers accurate predictions without correctly specifying the unknown true data generating process. A prior is defined over class of plausible predictive models. After observing data, we update to posterior these models, via criterion captures user-specified measure accuracy. Under regularity, this yields concentration onto elemen...

The Bayesian prediction of future failures from Lomax distribution is the subject this research. observed data censored using a Type-I hybrid censoring scheme under step-stress partially accelerated life test. There are two types sampling schemes considered: one-sample and two-sample. We create predictive intervals for failure observations in future. constructed MCMC algorithms. After all, nume...

We suggest a method for simultaneous variable selection and outlier identification based on the computation of posterior model probabilities. This avoids the problem that the model you select depends upon the order in which variable selection and outlier identification are carried out. Our method can find multiple outliers and appears to be successful in identifying masked outliers. We also add...

Besides good predictive performance, the naive Bayesian classifier can also offer a valuable insight into the structure of the training data and effects of the attributes on the class probabilities. This structure may be effectively revealed through visualization of the classifier. We propose a new way to visualize the naive Bayesian model in the form of a nomogram. The advantages of the propos...

Solar photovoltaic power (PV) generation has increased constantly in several countries in the last ten years becoming an important component of a sustainable solution of the energy problem. In this paper, a methodology to 24-hour or 48-hour photovoltaic power forecasting based on a Neural Network, trained in a Bayesian framework, is proposed. More specifically, an multi-ahead prediction Multi-L...