Multivariate Student importance sampling is a commonly used technique for Bayesian integration problems. For many problems, for example, when the inte-grand has ellipsoidal symmetry, it is a feasible and perhaps preferable, integration strategy. It is widely recognized , however, that the methodology suuers from limitations. We examine the methodology in conjunction with various variance reduct...

Capacity planning in the regeneration of complex capital goods faces major challenges because it is affected by a high level of uncertain workload information. A methodology is developed here to predict the regeneration workload on the basis of the CRISP-DM model using Bayesian networks. The forecasts are validated for the different capacity planning levels. The results support the conclusion t...

In this paper we present a novel Bayesian approach for default probability estimation. The methodology is based on multivariate contingent claim analysis and pair copula theory. Balance sheet data are used to asses the firm value and to compute its default probability. The firm pricing function is obtained via a pair copula approach, and Monte Carlo simulations are used to calculate the default...

A Bayesian method of estimating an unknown regression curve by a polynomial of random order is proposed. A joint distribution is assigned over both the set of possible orders of the polynomial and the polynomial coeecients. 1995) are used to compute required posteriors. The methodology is extended to polynomials of random order with discontinuties and to piecewise poly-nomials of random order. ...

With shrinking budgets and the requirements to increase reliability and operational life of the existing orbiter fleet, NASA has proposed various upgrades for the Space Shuttle that are consistent with national space policy. The cockpit avionics upgrade (CAU), a high priority item, has been selected as the next major upgrade. The primary functions of cockpit avionics include flight control, gui...

In this paper we review some recent advances on Bayesian methodology for performing Record Linkage and for making inference using the resulting matched units. In particular we frame the record linkage issue into a formal inferential problem and we adapt standard model selection techniques to this context. Although the methodology is quite general, we will focus on the simple multiple regression...

The methodology of Bayesian Model Averaging (BMA) is applied for assessment of newborn brain maturity from sleep EEG. In theory this methodology provides the most accurate assessments of uncertainty in decisions. However, the existing BMA techniques have been shown providing biased assessments in the absence of some prior information enabling to explore model parameter space in details within a...

Suppose that a dataset with N time series is available. N1 < N of those are the variables of interest. You want to estimate a vector autoregression (VAR) with the variables of interest. Which of the remaining N−N1 variables, if any, should you include in the VAR with the variables of interest? We develop a Bayesian methodology to answer this question. This question arises in most applications o...

In a probabilistic sensitivity analysis (PSA) of a cost-effectiveness (CE) study, the unknown parameters are considered as random variables. A crucial question is what probabilistic distribution is suitable for synthesizing the available information (mainly data from clinical trials) about these parameters. In this context, the important role of Bayesian methodology has been recognized, where t...