this paper describes two data analysis techniques adopted in a decision support system (dss), decline curve estimation and artificial neural network (ann) approaches, which aid users in predicting oil production of a field. the system generates different predictions, according to scenario, chosen for prediction. these two approaches show that to explain production of a field, ann method shows b...

b a c k g r o u n d & aim: the aim of the current study was to investigate the advantages of bayesian method in comparison to traditional methods to detect best antioxidant in freezing of human male gametes. methods  &  materials:   semen  samples  were  obtained  from  40  men  whose  sperm  had  normal criteria. a part of each sample was separated without antioxidant  as fresh and the remaini...

This paper applies a bayesian classification scheme to the problem of recognition through probabilistic modeling of high dimensional data. In this context, high dimensionality does not allow precision in the density estimation. We propose a local Independent Component Analysis (ICA) representation of the data. Here, the components can be assumed statistically independent and, in many cases, spa...

Intelligent software estimation models are need of the time. With increased development of Bayesian networks for software project management, one requires an explicit Bayesian Network (BN) to provide effort estimates based on historical data. This paper proposes a simple BN, based on classification approach. However the classes of ranges of size value, are distributed with help of fuzzification...

It is well known that a social choice function is truthfully implementable in Bayesian Nash equilibrium if and only if it is incentive compatible. However, in general it is not possible to rule out other equilibrium outcomes, and additional conditions, e.g., Bayesian monotonicity, are needed to ensure full implementation. We argue that this multiplicity problem is not very severe for virtual Ba...

Small–scale VARs are widely used in macroeconomics for forecasting U.S. output, prices, and interest rates. However, recent work suggests these models may exhibit instabilities. As such, a variety of estimation or forecasting methods might be used to improve their forecast accuracy. These include using different observation windows for estimation, intercept correction, time–varying parameters, ...

This paper proposes a novel Bayesian method for the dictionary learning (DL) based classification using Beta-Bernoulli process. We utilize this non-parametric Bayesian technique to learn jointly the sparse codes, the dictionary, and the classifier together. Existing DL based classification approaches only offer point estimation of the dictionary, the sparse codes, and the classifier and can the...

The probability approach to uncertainty and modeling is applied to default probability estimation. This issue has attracted attention as banks contemplate the requirements of Basel II’s IRB rules. Nicholas M. Kiefer proposes the fomal introduction of expert information into quantitative analysis. An application treating the incorporation of expert information on the default probability is consi...

this article examines the basic assumption of rational expectations hypothesis on iran's economy from 1966 to 2003. this assumption suggests the neutrality or ineffectiveness of money policy on the development of real variables of production and employment. the results from estimation of production equation do not support the significant influence of unanticipated component of money growth...