نتایج جستجو برای: using bayesian model
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A Bayesian network is a graphical model that represents a set of random variables and their causal relationship via a Directed Acyclic Graph (DAG). There are basically two methods used for learning Bayesian network: parameter-learning and structure-learning. One of the most effective structure-learning methods is K2 algorithm. Because the performance of the K2 algorithm depends on node...
type text or a website address or translate a document. abstract liquidity is considered the most important aspect of the development of stock markets. the main objective of this study was to evaluate the effect of the quality of financial information provided to replace its financial statements nmvdh and shrkt hayy that the liquidity of the shares on the tehran stock exchange is between the ...
Abstract: In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augme...
estimating the final price of products is of great importance. for manufacturing companies proposing a final price is only possible after the design process over. these companies propose an approximate initial price of the required products to the customers for which some of time and money is required. here using the existing data of already designed transformers and utilizing the bayesian anal...
background: colon cancer is the third cause of cancer deaths. although colon cancer survival time has increased in recent years, the mortality rate is still high. the cox model is the most common regression model often used in medical research in survival analysis, but most of the time the effect of at least one of the independent factors changes over time, so the model cannot be used. in the c...
Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for datasets where the number of variables p is large (e.g. p > 5, 000) the algorithm can become prohibitively expensive, computationally. Another method which is popular for hig...
We study the problem of learning Bayesian network structures from data. We develop an algorithm for finding the k-best Bayesian network structures. We propose to compute the posterior probabilities of hypotheses of interest by Bayesian model averaging over the k-best Bayesian networks. We present empirical results on structural discovery over several real and synthetic data sets and show that t...
this study considers the level of increase in customer satisfaction by supplying the variant customer requirements with respect to organizational restrictions. in this regard, anp, qfd and bgp techniques are used in a fuzzy set and a model is proposed in order to help the organization optimize the multi-objective decision-making process. the prioritization of technical attributes is the result ...
Background and Aim: Health surveillance systems are now paying more attention to infectious diseases, largely because of emerging and re-emerging infections. The main objective of this research is presenting a statistical method for modeling infectious disease incidence based on the Bayesian approach.Material and Methods: Since infectious diseases have two phases, namely epidemic and non-epidem...
The amount of total dissolved solids (TDS) is an important factor in stream engineering, especially study of river water quality. This study estimates the TDS amount of Belkhviachayriver in Ardabil Province, using bayesian neural network-, gene smart and artificial neural network. Quality variables include hydrogen carbonate, chloride, sulfate, calcium, magnesium, sodium and inflow (Q) in ...
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