Beliefs are the result of uncertainty. Sometimes uncertainty is because of a random process and sometimes the result of lack of information. In the past, the only solution in situations of uncertainty has been the probability theory. But the past few decades, various theories of other variables and systems are put forward for the systems with no adequate and accurate information. One of these a...

In this paper, the urinary infection, that is a common symptom of the decline of the immune system, is discussed based on the well-known algorithms in machine learning, such as Bayesian networks in both Markov and tree structures. A large scale sampling has been executed to evaluate the performance of Bayesian network algorithm. A number of 4052 samples wereobtained from the database of the Tak...

The identification of the most important factors affecting energy intensity with the aim of controlling and managing energy consumption is an important topic. Findings of different empirical studies on the factors affecting energy intensity are inconsistent and this raises uncertainty about the employed models. One of the techniques that conform to these uncertainty conditions of the model is t...

The current study was carried out to evaluate accuracy of some Bayesian methods for genomic breeding values prediction for threshold traits with different types of genetic architecture based on distribution of gene effect and QTL numbers. A genome consisted of 3 chromosomes of 100 CM with 2000 single nucleotide polymorphisms (SNP) was simulated. The QTL numbers were 0.01, 0.05 and 0.1 of total ...

Background and objective: Numerous studies have reported beneficial effects of smoking cessation in terms of decreased cardiovascular mortality in patients with coronary heart disease. This paper aimed to determine a valid estimate for the relative risk of mortality in subjects who quit smoking compared to those continued smoking. Methods: All relevant prospective cohort studies of chronic hear...